http://ixa2.si.ehu.es/deep_learning_seminar/
Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language and vision. Image captioning, visual question answering or multimodal translation are some of the first applications of a new and exciting field that exploiting the generalization properties of deep neural representations. This talk will provide an overview of how vision and language problems are addressed with deep neural networks, and the exciting challenges being addressed nowadays by the research community.
https://telecombcn-dl.github.io/2019-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://mcv-m6-video.github.io/deepvideo-2019/
These slides provides an overview of how deep neural networks can be used to solve an object tracking task
https://github.com/mcv-m6-video/deepvideo-2019
The synchronization of the visual and audio tracks recorded in videos can be used as a supervisory signal for machine learning. This presentation reviews some recent research on this topic exploiting the capabilities of deep neural networks.
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://mcv-m6-video.github.io/deepvideo-2019/
Overview of deep learning solutions for video processing. Part of a series of slides covering topics like action recognition, action detection, object tracking, object detection, scene segmentation, language and learning from videos.
Master in Computer Vision Barcelona, 2019
Giro-i-Nieto, X. One Perceptron to Rule Them All: Language, Vision, Audio and Speech. In Proceedings of the 2020 International Conference on Multimedia Retrieval (pp. 7-8).
Tutorial page:
https://imatge.upc.edu/web/publications/one-perceptron-rule-them-all-language-vision-audio-and-speech-tutorial
Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language, vision and speech. Image captioning, lip reading or video sonorization are some of the first applications of a new and exciting field of research exploiting the generalization properties of deep neural representation. This tutorial will firstly review the basic neural architectures to encode and decode vision, text and audio, to later review the those models that have successfully translated information across modalities.
https://mcv-m6-video.github.io/deepvideo-2020/
Self-supervised audiovisual learning exploits the synchronization between pixels and audio recorded in video files. This lecture reviews the state of the art in deep neural networks trained with this approach, which does not require any manual annotation from humans.
https://telecombcn-dl.github.io/2019-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://mcv-m6-video.github.io/deepvideo-2019/
These slides provides an overview of how deep neural networks can be used to solve an object tracking task
https://github.com/mcv-m6-video/deepvideo-2019
The synchronization of the visual and audio tracks recorded in videos can be used as a supervisory signal for machine learning. This presentation reviews some recent research on this topic exploiting the capabilities of deep neural networks.
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://mcv-m6-video.github.io/deepvideo-2019/
Overview of deep learning solutions for video processing. Part of a series of slides covering topics like action recognition, action detection, object tracking, object detection, scene segmentation, language and learning from videos.
Master in Computer Vision Barcelona, 2019
Giro-i-Nieto, X. One Perceptron to Rule Them All: Language, Vision, Audio and Speech. In Proceedings of the 2020 International Conference on Multimedia Retrieval (pp. 7-8).
Tutorial page:
https://imatge.upc.edu/web/publications/one-perceptron-rule-them-all-language-vision-audio-and-speech-tutorial
Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language, vision and speech. Image captioning, lip reading or video sonorization are some of the first applications of a new and exciting field of research exploiting the generalization properties of deep neural representation. This tutorial will firstly review the basic neural architectures to encode and decode vision, text and audio, to later review the those models that have successfully translated information across modalities.
https://mcv-m6-video.github.io/deepvideo-2020/
Self-supervised audiovisual learning exploits the synchronization between pixels and audio recorded in video files. This lecture reviews the state of the art in deep neural networks trained with this approach, which does not require any manual annotation from humans.
https://imatge-upc.github.io/wav2pix/
Speech is a rich biometric signal that contains information about the identity, gender and emotional state of the speaker. In this work, we explore its potential to generate face images of a speaker by conditioning a Generative Adversarial Network (GAN) with raw speech input. We propose a deep neural network that is trained from scratch in an end-to-end fashion, generating a face directly from the raw speech waveform without any additional identity information (e.g reference image or one-hot encoding). Our model is trained in a self-supervised fashion by exploiting the audio and visual signals naturally aligned in videos. With the purpose of training from video data, we present a novel dataset collected for this work, with high-quality videos of ten youtubers with notable expressiveness in both the speech and visual signals.
https://mcv-m6-video.github.io/deepvideo-2019/
Overview of deep learning solutions for video processing. Part of a series of slides covering topics like action recognition, action detection, object tracking, object detection, scene segmentation, language and learning from videos.
Deep neural networks have revolutionized the data analytics scene by improving results in several and diverse benchmarks with the same recipe: learning feature representations from data. These achievements have raised the interest across multiple scientific fields, especially in those where large amounts of data and computation are available. This change of paradigm in data analytics has several ethical and economic implications that are driving large investments, political debates and sounding press coverage under the generic label of artificial intelligence (AI). This talk will present the fundamentals of deep learning through the classic example of image classification, and point at how the same principal has been adopted for several tasks. Finally, some of the forthcoming potentials and risks for AI will be pointed.
Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language, vision and speech. Image captioning, lip reading or video sonorization are some of the first applications of a new and exciting field of research exploiting the generalization properties of deep neural representation. This tutorial will firstly review the basic neural architectures to encode and decode vision, text and audio, to later review the those models that have successfully translated information across modalities. The contents of this tutorial are available at: https://telecombcn-dl.github.io/2019-mmm-tutorial/.
These slides review the research of our lab since 2016 on applied deep learning, starting from our participation in the TRECVID Instance Search 2014, moving into video analysis with CNN+RNN architectures, and our current efforts in sign language translation and production.
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
These slides summarize the main trends in deep neural networks for video encoding. Including single frame models, spatiotemporal convolutionals, long term sequence modeling with RNNs and their combinaction with optical flow.
https://mcv-m6-video.github.io/deepvideo-2018/
Overview of deep learning solutions for video processing. Part of a series of slides covering topics like action recognition, action detection, object tracking, object detection, scene segmentation, language and learning from videos.
Prepared for the Master in Computer Vision Barcelona:
http://pagines.uab.cat/mcv/
https://imatge.upc.edu/web/publications/video-saliency-prediction-deep-neural-networks
Saliency prediction is a topic undergoing intense study in computer vision with a broad range of applications. It consists in predicting where the attention is going to be received in an image or a video by a human. Our work is based on a deep neural network named SalGAN, which was trained on a saliency annotated dataset of static images. In this thesis we investigate different approaches for extending SalGAN to the video domain. To this end, we investigate the recently proposed saliency annotated video dataset DHF1K to train and evaluate our models. The obtained results indicate that techniques such as depth estimation or coordconv can effectively be used as additional modalities to enhance the saliency prediction of static images obtained with SalGAN, achieving encouraging results in the DHF1K benchmark. Our work is based on pytorch and it is publicly available here.
https://mcv-m6-video.github.io/deepvideo-2018/
Overview of deep learning solutions for video processing. Part of a series of slides covering topics like action recognition, action detection, object tracking, object detection, scene segmentation, language and learning from videos.
Prepared for the Master in Computer Vision Barcelona:
http://pagines.uab.cat/mcv/
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://mcv-m6-video.github.io/deepvideo-2018/
Overview of deep learning solutions for video processing. Part of a series of slides covering topics like action recognition, action detection, object tracking, object detection, scene segmentation, language and learning from videos.
Prepared for the Master in Computer Vision Barcelona:
http://pagines.uab.cat/mcv/
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://telecombcn-dl.github.io/dlmm-2017-dcu/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://mcv-m6-video.github.io/deepvideo-2018/
Overview of deep learning solutions for video processing. Part of a series of slides covering topics like action recognition, action detection, object tracking, object detection, scene segmentation, language and learning from videos.
Prepared for the Master in Computer Vision Barcelona:
http://pagines.uab.cat/mcv/
This lecture reviews methods that allow interpreting the outcomes of a deep convolutional neural network. It presents some of the techniques proposed in the literature.
Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language, vision and speech. Image captioning, lip reading or video sonorization are some of the first applications of a new and exciting field of research exploiting the generalization properties of deep learning. In this talk we will review the latest results on how convolutional and recurrent neural networks are combined to find the most hidden patterns in multimedia.
Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language, vision and speech. Image captioning, lip reading or video sonorization are some of the first applications of a new and exciting field of research exploiting the generalisation properties of deep learning. Get the latest results on how convolutional and recurrent neural networks are combined to find the most hidden patterns in multimedia.
https://re-work.co/events/deep-learning-summit-london-2017/
https://imatge-upc.github.io/wav2pix/
Speech is a rich biometric signal that contains information about the identity, gender and emotional state of the speaker. In this work, we explore its potential to generate face images of a speaker by conditioning a Generative Adversarial Network (GAN) with raw speech input. We propose a deep neural network that is trained from scratch in an end-to-end fashion, generating a face directly from the raw speech waveform without any additional identity information (e.g reference image or one-hot encoding). Our model is trained in a self-supervised fashion by exploiting the audio and visual signals naturally aligned in videos. With the purpose of training from video data, we present a novel dataset collected for this work, with high-quality videos of ten youtubers with notable expressiveness in both the speech and visual signals.
https://mcv-m6-video.github.io/deepvideo-2019/
Overview of deep learning solutions for video processing. Part of a series of slides covering topics like action recognition, action detection, object tracking, object detection, scene segmentation, language and learning from videos.
Deep neural networks have revolutionized the data analytics scene by improving results in several and diverse benchmarks with the same recipe: learning feature representations from data. These achievements have raised the interest across multiple scientific fields, especially in those where large amounts of data and computation are available. This change of paradigm in data analytics has several ethical and economic implications that are driving large investments, political debates and sounding press coverage under the generic label of artificial intelligence (AI). This talk will present the fundamentals of deep learning through the classic example of image classification, and point at how the same principal has been adopted for several tasks. Finally, some of the forthcoming potentials and risks for AI will be pointed.
Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language, vision and speech. Image captioning, lip reading or video sonorization are some of the first applications of a new and exciting field of research exploiting the generalization properties of deep neural representation. This tutorial will firstly review the basic neural architectures to encode and decode vision, text and audio, to later review the those models that have successfully translated information across modalities. The contents of this tutorial are available at: https://telecombcn-dl.github.io/2019-mmm-tutorial/.
These slides review the research of our lab since 2016 on applied deep learning, starting from our participation in the TRECVID Instance Search 2014, moving into video analysis with CNN+RNN architectures, and our current efforts in sign language translation and production.
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
These slides summarize the main trends in deep neural networks for video encoding. Including single frame models, spatiotemporal convolutionals, long term sequence modeling with RNNs and their combinaction with optical flow.
https://mcv-m6-video.github.io/deepvideo-2018/
Overview of deep learning solutions for video processing. Part of a series of slides covering topics like action recognition, action detection, object tracking, object detection, scene segmentation, language and learning from videos.
Prepared for the Master in Computer Vision Barcelona:
http://pagines.uab.cat/mcv/
https://imatge.upc.edu/web/publications/video-saliency-prediction-deep-neural-networks
Saliency prediction is a topic undergoing intense study in computer vision with a broad range of applications. It consists in predicting where the attention is going to be received in an image or a video by a human. Our work is based on a deep neural network named SalGAN, which was trained on a saliency annotated dataset of static images. In this thesis we investigate different approaches for extending SalGAN to the video domain. To this end, we investigate the recently proposed saliency annotated video dataset DHF1K to train and evaluate our models. The obtained results indicate that techniques such as depth estimation or coordconv can effectively be used as additional modalities to enhance the saliency prediction of static images obtained with SalGAN, achieving encouraging results in the DHF1K benchmark. Our work is based on pytorch and it is publicly available here.
https://mcv-m6-video.github.io/deepvideo-2018/
Overview of deep learning solutions for video processing. Part of a series of slides covering topics like action recognition, action detection, object tracking, object detection, scene segmentation, language and learning from videos.
Prepared for the Master in Computer Vision Barcelona:
http://pagines.uab.cat/mcv/
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://mcv-m6-video.github.io/deepvideo-2018/
Overview of deep learning solutions for video processing. Part of a series of slides covering topics like action recognition, action detection, object tracking, object detection, scene segmentation, language and learning from videos.
Prepared for the Master in Computer Vision Barcelona:
http://pagines.uab.cat/mcv/
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://telecombcn-dl.github.io/dlmm-2017-dcu/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://mcv-m6-video.github.io/deepvideo-2018/
Overview of deep learning solutions for video processing. Part of a series of slides covering topics like action recognition, action detection, object tracking, object detection, scene segmentation, language and learning from videos.
Prepared for the Master in Computer Vision Barcelona:
http://pagines.uab.cat/mcv/
This lecture reviews methods that allow interpreting the outcomes of a deep convolutional neural network. It presents some of the techniques proposed in the literature.
Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language, vision and speech. Image captioning, lip reading or video sonorization are some of the first applications of a new and exciting field of research exploiting the generalization properties of deep learning. In this talk we will review the latest results on how convolutional and recurrent neural networks are combined to find the most hidden patterns in multimedia.
Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language, vision and speech. Image captioning, lip reading or video sonorization are some of the first applications of a new and exciting field of research exploiting the generalisation properties of deep learning. Get the latest results on how convolutional and recurrent neural networks are combined to find the most hidden patterns in multimedia.
https://re-work.co/events/deep-learning-summit-london-2017/
Language and speech technologies are rapidly evolving thanks to the current advances in artificial intelligence. The convergence of large-scale datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Applications such as machine translation or speech recognition can be tackled from a neural perspective with novel architectures that combine convolutional and/or recurrent models with attention. This winter school overview the state of the art on deep learning for speech and language ad introduces the programming skills and techniques required to train these systems.
Machine translation and computer vision have greatly benefited from the advances in deep learning. A large and diverse amount of textual and visual data have been used to train neural networks whether in a supervised or self-supervised manner. Nevertheless, the convergence of the two fields in sign language translation and production still poses multiple open challenges, like the low video resources, limitations in hand pose estimation, or 3D spatial grounding from poses.
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Modeling perceptual similarity and shift invariance in deep networksNAVER Engineering
Abstract: While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification have been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called "perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations.
Despite their strong transfer performance, deep convolutional representations surprisingly lack a basic low-level property -- shift-invariance, as small input shifts or translations can cause drastic changes in the output. Commonly used downsampling methods, such as max-pooling, strided-convolution, and average-pooling, ignore the sampling theorem. The well-known signal processing fix is anti-aliasing by low-pass filtering before downsampling. However, simply inserting this module into deep networks degrades performance; as a result, it is seldomly used today. We show that when integrated correctly, it is compatible with existing architectural components, such as max-pooling and strided-convolution. We observe increased accuracy in ImageNet classification, across several commonly-used architectures, such as ResNet, DenseNet, and MobileNet, indicating effective regularization. Furthermore, we observe better generalization, in terms of stability and robustness to input corruptions. Our results demonstrate that this classical signal processing technique has been undeservingly overlooked in modern deep networks.
https://telecombcn-dl.github.io/2018-dlmm/
achine Learning and deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. Ever wondered what all the fuss is about? Or what these technologies can do for you? Are you working in the field and wish to enhance your current knowledge in some specific techniques?
Insight@DCU will host a 2 day workshop on Machine Learning on May 21st and 22nd, which will help to answer your questions, whether a novice or knowledgeable in the field.
This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Deep neural networks have revolutionized the data analytics scene by improving results in several and diverse benchmarks with the same recipe: learning feature representations from data. These achievements have raised the interest across multiple scientific fields, especially in those where large amounts of data and computation are available. This change of paradigm in data analytics has several ethical and economic implications that are driving large investments, political debates and sounding press coverage under the generic label of artificial intelligence (AI). This talk will present the fundamentals of deep learning through the classic example of image classification, and point at how the same principal has been adopted for several tasks. Finally, some of the forthcoming potentials and risks for AI will be pointed.
International Perspectives: Visualization in Science and EducationLiz Dorland
Overview of the international and interdisciplinary Gordon Research Conference on Visualization in Science and Education and info on key cognitive science and learning sciences researchers. History of the conference, NSF workshop, and research on learning with visualizations.
Multimedia Information Retrieval: Bytes and pixels meet the challenges of hum...maranlar
Within computer science, "Multimedia" is a field of research that investigates how computers can support people in communication, information finding, and knowledge/opinion building. Multimedia content is defined broadly. It includes not only video, but also images accompanied by text and other information (for example, a geo-location). It can be professionally produced, or generated by users for online sharing. Computer scientists historically have a “love-hate” relationship with multimedia. They “love” it because of the richness of the data sources and the wealth of available data, which leads to interesting problems to tackle with machine learning. They “hate” it because multimedia is a diffuse and moving target: the interpretation of multimedia differs from person to person, and changes over time in the course of its use as a communication medium. This talk gives a view onto ongoing research in the area of multimedia information retrieval algorithms, which help people find multimedia. We look at a series of topics that reveal how pattern recognition, text processing, and crowdsourcing tools are used in multimedia research, and discuss both their limitations and their potential.
2019년 파이콘 한국에서 진행된 튜토리얼 자료입니다. 최재식 교수님께서 설명가능인공지능이란 무엇인가에 대해 발표해주신 Part 1 발표자료입니다. 아래 링크를 통해 행사 관련 정보를 확인하실 수 있습니다.
http://xai.unist.ac.kr/Tutorial/2018/
https://github.com/OpenXAIProject/PyConKorea2019-Tutorials
Part 1: https://www.slideshare.net/OpenXAI/2019-part-1
Part 2: https://www.slideshare.net/OpenXAI/2019-lrp-part-2
Part 3: https://www.slideshare.net/OpenXAI/2019-shap-part-3
Machine translation and computer vision have greatly benefited of the advances in deep learning. The large and diverse amount of textual and visual data have been used to train neural networks whether in a supervised or self-supervised manner. Nevertheless, the convergence of the two field in sign language translation and production is still poses multiple open challenges, like the low video resources, limitations in hand pose estimation, or 3D spatial grounding from poses. This talk will present these challenges and the How2✌️Sign dataset (https://how2sign.github.io) recorded at CMU in collaboration with UPC, BSC, Gallaudet University and Facebook.
https://imatge.upc.edu/web/publications/sign-language-translation-and-production-multimedia-and-multimodal-challenges-all
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://youtu.be/3FE2HhQnFh0
I am thankful for the CZI scholarship as a DeepLabCut AI resident to study the neuroscience of sexual diversity. DeepLabCut is a deep-learning-based open-source toolbox for 3D pose estimation. It has been used in a wide range of applications e.g. chicken agriculture, surgery, dog poop detector, infant exploratory behaviour, lizard robotics, exergaming biofeedback, 3D triangulation of cheetahs chasing prey, spider webbing, stroke rehabilitation, wildlife conservation, pupil tracking, parrot tripedal locomotion, fear behaviour, dog emotions, functional recovery after spinal cord injury etc.
http://www.mackenziemathislab.org/deeplabcut
A behaviomics approach like DeepLabCut significantly benefits my research on sexual behaviour in a steroid-independent and collective behaviour context. Traditional methods are limited by their subjectivity, biased in selecting parameters to measure, and extremely labour-intensive and time-consuming. There are also limits to human perception and language to accurately detect and describe behaviour. At a broader level, behaviomics improves animal ethics and biodiversity. For animal ethics, behaviomics increase the accuracy and throughput of the data, which reduces the number of animals for the same amount of data. These data also contribute to developing in silico and robotic models that can replace animal experiments. For biodiversity, behaviomics allows researchers to move away from behaviour recordings in the lab in “labesticated” animal models and captive species. More wildlife in naturalistic settings can be studied including footage from drones and satellites.
https://www.nature.com/articles/s41467-022-27980-y
From the experiences of people like Deborah Raji and Timnit Gebru, we know the field of AI is dominated by and predominantly serves the WEIRD (Western, educated, industrialized, rich and democratic) population, particularly white, cisgender, and heterosexual males. It excludes marginalised minorities from its creations which led to race and gender misidentification problems, as well as resulting in the “weapons of math destruction”, as coined by Cathy O'Neil. We need to improve this by embracing perspectives beyond Western science, for example, by incorporating Indigenous communities and Arabic philosophies. There’s also a hegemony of software licensing that provides additional economic barriers to access. DeepLabCut hopes to reduce this barrier by being open source.
https://www.currentaffairs.org/.../software-licesing-is-a...
This residency drives forward my research on the neuroscience of sexual diversity and trains me to become an open-source code contributor. Learning from approaches like EarSketch, Queer in AI, and Black Girls Code, this residency also helps me diversify AI through assisting marginalised minorities to learn AI and become code contributors as well. There are many people to thank for this opportunity. https://www.deeplabcutairesidency.org/our-team
Deep learning is having a profound impact on AI applications. With the future of neural network-inspired computing in mind, re:Invent is hosting the first ever Deep Learning Summit. Designed for developers to learn about the latest in deep learning research and emerging trends, attendees will hear from industry thought leaders—members of the academic and venture capital communities—who will share their perspectives in 30-minute Lightning Talks.
The Summit will be held on Thursday, November 30th at the Venetian from 1-5pm.
The Deep Learning Revolution - Terrence Sejnowski, The Salk Institute for Biological Studies
Eye, Robot: Computer Vision and Autonomous Robotics - Aaron Ames & Pietro Perona, California Institute of Technology
Exploiting the Power of Language - Alexander Smola, Amazon Web Services
Reducing Supervision: Making More with Less - Martial Herbert, Carnegie Mellon University
Learning Where to Look in Video - Kristen Grauman, University of Texas
Look, Listen, Learn: The Intersection of Vision and Sound - Antonio Torralba, MIT
Investing in the Deep Learning Future - Matt Ocko, Data Collective Venture Capital
https://imatge.upc.edu/web/people/xavier-giro
These slides provide an overview of our research group at UPC, which has been applying deep learning to computer vision since 2014. We are one of the pioneering research groups in Europe and, despite the youth of most of its member, it has already contributed to the community with a diverse range of publications and software at top scientific venues.
This document provides an overview of deep generative learning and summarizes several key generative models including GANs, VAEs, diffusion models, and autoregressive models. It discusses the motivation for generative models and their applications such as image generation, text-to-image synthesis, and enhancing other media like video and speech. Example state-of-the-art models are provided for each application. The document also covers important concepts like the difference between discriminative and generative modeling, sampling techniques, and the training procedures for GANs and VAEs.
The transformer is the neural architecture that has received most attention in the early 2020's. It removed the recurrency in RNNs, replacing it with and attention mechanism across the input and output tokens of a sequence (cross-attenntion) and between the tokens composing the input (and output) sequences, named self-attention.
https://imatge-upc.github.io/synthref/
Integrating computer vision with natural language processing has achieved significant progress
over the last years owing to the continuous evolution of deep learning. A novel vision and language
task, which is tackled in the present Master thesis is referring video object segmentation, in which a
language query defines which instance to segment from a video sequence. One of the biggest chal-
lenges for this task is the lack of relatively large annotated datasets since a tremendous amount of
time and human effort is required for annotation. Moreover, existing datasets suffer from poor qual-
ity annotations in the sense that approximately one out of ten language expressions fails to uniquely
describe the target object.
The purpose of the present Master thesis is to address these challenges by proposing a novel
method for generating synthetic referring expressions for an image (video frame). This method pro-
duces synthetic referring expressions by using only the ground-truth annotations of the objects as well
as their attributes, which are detected by a state-of-the-art object detection deep neural network. One
of the advantages of the proposed method is that its formulation allows its application to any object
detection or segmentation dataset.
By using the proposed method, the first large-scale dataset with synthetic referring expressions for
video object segmentation is created, based on an existing large benchmark dataset for video instance
segmentation. A statistical analysis and comparison of the created synthetic dataset with existing ones
is also provided in the present Master thesis.
The conducted experiments on three different datasets used for referring video object segmen-
tation prove the efficiency of the generated synthetic data. More specifically, the obtained results
demonstrate that by pre-training a deep neural network with the proposed synthetic dataset one can
improve the ability of the network to generalize across different datasets, without any additional annotation cost. This outcome is even more important taking into account that no additional annotation cost is involved.
Master MATT thesis defense by Juan José Nieto
Advised by Víctor Campos and Xavier Giro-i-Nieto.
27th May 2021.
Pre-training Reinforcement Learning (RL) agents in a task-agnostic manner has shown promising results. However, previous works still struggle to learn and discover meaningful skills in high-dimensional state-spaces. We approach the problem by leveraging unsupervised skill discovery and self-supervised learning of state representations. In our work, we learn a compact latent representation by making use of variational or contrastive techniques. We demonstrate that both allow learning a set of basic navigation skills by maximizing an information theoretic objective. We assess our method in Minecraft 3D maps with different complexities. Our results show that representations and conditioned policies learned from pixels are enough for toy examples, but do not scale to realistic and complex maps. We also explore alternative rewards and input observations to overcome these limitations.
https://imatge.upc.edu/web/publications/discovery-and-learning-navigation-goals-pixels-minecraft
Peter Muschick MSc thesis
Universitat Pollitecnica de Catalunya, 2020
Sign language recognition and translation has been an active research field in the recent years with most approaches using deep neural networks to extract information from sign language data. This work investigates the mostly disregarded approach of using human keypoint estimation from image and video data with OpenPose in combination with transformer network architecture. Firstly, it was shown that it is possible to recognize individual signs (4.5% word error rate (WER)). Continuous sign language recognition though was more error prone (77.3% WER) and sign language translation was not possible using the proposed methods, which might be due to low accuracy scores of human keypoint estimation by OpenPose and accompanying loss of information or insufficient capacities of the used transformer model. Results may improve with the use of datasets containing higher repetition rates of individual signs or focusing more precisely on keypoint extraction of hands.
https://github.com/telecombcn-dl/lectures-all/
These slides review techniques for interpreting the behavior of deep neural networks. The talk reviews basic techniques such as the display of filters and tensors, as well as more advanced ones that try to interpret which part of the input data is responsible for the predictions, or generate data that maximizes the activation of certain neurons.
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/dlai-2020/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/drl-2020/
This course presents the principles of reinforcement learning as an artificial intelligence tool based on the interaction of the machine with its environment, with applications to control tasks (eg. robotics, autonomous driving) o decision making (eg. resource optimization in wireless communication networks). It also advances in the development of deep neural networks trained with little or no supervision, both for discriminative and generative tasks, with special attention on multimedia applications (vision, language and speech).
Image segmentation is a classic computer vision task that aims at labeling pixels with semantic classes. These slides provide an overview of the basic approaches applied from the deep learning field to tackle this challenge and presents the basic subtasks (semantic, instance and panoptic segmentation) and related datasets.
Presented at the International Summer School on Deep Learning (ISSonDL) 2020 held online and organized by the University of Gdansk (Poland) between the 30th August and 2nd September.
http://2020.dl-lab.eu/virtual-summer-school-on-deep-learning/
https://imatge-upc.github.io/rvos-mots/
Video object segmentation can be understood as a sequence-to-sequence task that can benefit from the curriculum learning strategies for better and faster training of deep neural networks. This work explores different schedule sampling and frame skipping variations to significantly improve the performance of a recurrent architecture. Our results on the car class of the KITTI-MOTS challenge indicate that, surprisingly, an inverse schedule sampling is a better option than a classic forward one. Also, that a progressive skipping of frames during training is beneficial, but only when training with the ground truth masks instead of the predicted ones.
Deep neural networks have achieved outstanding results in various applications such as vision, language, audio, speech, or reinforcement learning. These powerful function approximators typically require large amounts of data to be trained, which poses a challenge in the usual case where little labeled data is available. During the last year, multiple solutions have been proposed to leverage this problem, based on the concept of self-supervised learning, which can be understood as a specific case of unsupervised learning. This talk will cover its basic principles and provide examples in the field of multimedia.
Benet Oriol, Jordi Luque, Ferran Diego, Xavier Giro-i-Nieto
Telefonica Research / Universitat Politecnica de Catalunya (UPC)
CVPR 2020 Workshop on on Egocentric Perception, Interaction and Computing
In this work, we propose an effective approach for training unique embedding representations by combining three simultaneous modalities: image and spoken and textual narratives. The proposed methodology departs from a baseline system that spawns a embedding space trained with only spoken narratives and image cues. Our experiments on the EPIC-Kitchen and Places Audio Caption datasets show that introducing the human-generated textual transcriptions of the spoken narratives helps to the training procedure yielding to get better embedding representations. The triad speech, image and words allows for a better estimate of the point embedding and show an improving of the performance within tasks like image and speech retrieval, even when text third modality, text, is not present in the task.
These slides provide an overview of the most popular approaches up to date to solve the task of object detection with deep neural networks. It reviews both the two stages approaches such as R-CNN, Fast R-CNN and Faster R-CNN, and one-stage approaches such as YOLO and SSD. It also contains pointers to relevant datasets (Pascal, COCO, ILSRVC, OpenImages) and the definition of the Average Precision (AP) metric.
Full program:
https://www.talent.upc.edu/ing/estudis/formacio/curs/310400/postgraduate-course-artificial-intelligence-deep-learning/
https://mcv-m6-video.github.io/deepvideo-2020/
Self-supervised techniques define surrogate tasks to train machine learning algorithms without the need of human generated labels. This lecture reviews the state of the art in the field of computer vision, including the baseline techniques based on visual feature learning from ImageNet data.
This lecture provides an introduction to recurrent neural networks, which include a layer whose hidden state is aware of its values in a previous time-step.
These slides were used in the Master in Computer Vision Barcelona 2019/2020, in the Module 6 dedicated to Video Analysis.
http://pagines.uab.cat/mcv/
https://telecombcn-dl.github.io/idl-2020/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
More from Universitat Politècnica de Catalunya (20)
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
One Perceptron to Rule Them All: Language and Vision
1. One Perceptron to Rule Them All:
Language and Vision
Xavier Giro-i-Nieto
xavier.giro@upc.edu
Associate Professor
Intelligent Data Science and Artificial
Intelligence Center (IDEAI)
Universitat Politecnica de Catalunya (UPC)
Barcelona Supercomputing Center (BSC)
Deep Learning
for Natural
Language
Processing
San Sebastian
5 July 2019
bit.ly/ixa-dlnlp-2019
xavier.giro@upc.edu
@DocXavi
3. 3
● 11 faculty members
● 12 Phd students
Research Group & Centers
https://imatge.upc.edu/
https://www.bsc.es/
● National computation center #1
● Supercomputer MareNostrum
● Emerging Technologies for
Artificial Intelligence Group,
directed by Prof. Jordi Torres.
https://ideai.upc.edu/
● Center funded in 2017
● 60 researchers
IDEAI (Intelligent Data Science and
Artificial Intelligence)
24. 24
Pooling Layer
Figure Credit: Ranzatto
Pooling is a downsample operation
along the spatial dimensions (width,
height)
● It reduces progressively the
spatial size of the
representation, so it reduces the
computation greatly.
● Provides invariance to small
local changes
25. 25
Pooling Layer (critics)
"The pooling operation
used in CNNs is a big
mistake and the fact that it
works so well is a disaster."
Geoffrey Hinton,
AMA reddit (2015).
Learn more:
Richard Zhang, “Making Convolutional Networks Shift-Invariant Again” (ICML 2019)
26. 26
Convolutional Neural Networks for Vision
LeNet-5: Several convolutional layers, combined with pooling layers, and followed by a
small number of fully connected layers
#LeNet-5 LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document
recognition. Proceedings of the IEEE, 86(11), 2278-2324.
27. 27
ImageNet Challenge
● 1,000 object classes
(categories).
● Images:
○ 1.2 M train
○ 100k test.
Deng, Jia, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. "Imagenet: A large-scale hierarchical image
database." CVPR 2019.
28. 28
ImageNet Challenge: 2012
Slide credit:
Rob Fergus (NYU)
-9.8%
Based on SIFT + Fisher Vectors
Russakovsky, Olga, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang et al. "Imagenet
large scale visual recognition challenge." International Journal of Computer Vision 115, no. 3 (2015): 211-252. [web]
29. 29
Image Encoding
A Krizhevsky, I Sutskever, GE Hinton “Imagenet classification with deep convolutional neural networks” NIPS 2012
Cat
CNN FC
31. 31
Video Encoding
Slide: Víctor Campos (UPC 2018)
CNN CNN CNN...
Combination method
Combination is commonly
implemented as a small NN on
top of a pooling operation
(e.g. max, sum, average).
Drawback: pooling is not
aware of the temporal order!
Ng et al., Beyond short snippets: Deep networks for video classification, CVPR 2015
32. 32
Video Encoding
Slide: Víctor Campos (UPC 2018)
Recurrent Neural Networks are
well suited for processing
sequences.
Drawback: RNNs are sequential
and cannot be parallelized.
Donahue et al., Long-term Recurrent Convolutional Networks for Visual Recognition and Description, CVPR 2015
CNN CNN CNN...
RNN RNN RNN...
41. 41
#ShowAndTell Vinyals, Oriol, Alexander Toshev, Samy Bengio, and Dumitru Erhan. "Show and tell: A neural image caption
generator." CVPR 2015.
Image Captioning
43. 43
Captioning: Show, Attend & Tell
Xu, Kelvin, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, and Yoshua
Bengio. "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention." ICML 2015
44. 44
Captioning: Show, Attend & Tell
Xu, Kelvin, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, and Yoshua
Bengio. "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention." ICML 2015
45. 45
Johnson, Justin, Andrej Karpathy, and Li Fei-Fei. "Densecap: Fully convolutional localization networks for dense
captioning." CVPR 2016
Dense Captioning
46. 46
XAVI: “man has
short hair”, “man
with short hair”
AMAIA:”a woman
wearing a black
shirt”, “
BOTH: “two men
wearing black
glasses”
Johnson, Justin, Andrej Karpathy, and Li Fei-Fei. "Densecap: Fully convolutional localization networks for dense
captioning." CVPR 2016
Dense Captioning
47. Image Captioning for News
Ali Furkan Biten, Lluis Gomez, Marçal Rusiñol, Dimosthenis Karatzas, “Good News, Everyone! Context driven entity-aware
captioning for news images” CVPR 2019.
48. 48
Filtering Social Bias in Neural Models
#Equalizer Burns, Kaylee, Lisa Anne Hendricks, Trevor Darrell, and Anna Rohrbach. "Women also Snowboard: Overcoming
Bias in Captioning Models." ECCV 2018.
49. 49
Captioning: Dataset biases
#Equalizer Burns, Kaylee, Lisa Anne Hendricks, Trevor Darrell, and Anna Rohrbach. "Women also Snowboard: Overcoming
Bias in Captioning Models." ECCV 2018.
50. 50
Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor
Darrel. Long-term Recurrent Convolutional Networks for Visual Recognition and Description, CVPR 2015. code
Captioning: Video
51. 51
(Slides by Marc Bolaños) Pingbo Pan, Zhongwen Xu, Yi Yang,Fei Wu,Yueting Zhuang Hierarchical Recurrent Neural
Encoder for Video Representation with Application to Captioning, CVPR 2016.
LSTM unit
(2nd layer)
Time
Image
t = 1 t = T
hidden state
at t = T
first chunk
of data
Captioning: Video
53. 53
Assael, Yannis M., Brendan Shillingford, Shimon Whiteson, and Nando de Freitas. "LipNet: End-to-End Sentence-level Lipreading."
(2016).
54. 54
Lip Reading
Assael, Yannis M., Brendan Shillingford, Shimon Whiteson, and Nando de Freitas. "LipNet: End-to-End Sentence-level
Lipreading." (2016).
55. 55
Chung, Joon Son, Andrew Senior, Oriol Vinyals, and Andrew Zisserman. "Lip reading sentences in the wild."
CVPR 2017
56. 56
Lipreading: Watch, Listen, Attend & Spell
Audio
features
Image
features
Chung, Joon Son, Andrew Senior, Oriol Vinyals, and Andrew Zisserman. "Lip reading sentences in the wild." CVPR 2017
57. 57
Lipreading: Watch, Listen, Attend & Spell
Chung, Joon Son, Andrew Senior, Oriol Vinyals, and Andrew Zisserman. "Lip reading sentences in the wild." CVPR 2017
Attention over output
states from audio and
video is computed at
each timestep
59. 59
Grounded Captioning from Objects
Lu, Jiasen and Yang, Jianwei and Batra, Dhruv and Parikh, Devi “Neural Baby Talk” CVPR 2018 [code]
60. 60Lu, Jiasen and Yang, Jianwei and Batra, Dhruv and Parikh, Devi “Neural Baby Talk” CVPR 2018 [code]
Grounded Captioning from Objects
61. 61Akbari, Hassan, Svebor Karaman, Surabhi Bhargava, Brian Chen, Carl Vondrick, and Shih-Fu Chang. "Multi-level Multimodal
Common Semantic Space for Image-Phrase Grounding." CVPR 2019. [code]
Weak grounding w/o supervision
62. 62Akbari, Hassan, Svebor Karaman, Surabhi Bhargava, Brian Chen, Carl Vondrick, and Shih-Fu Chang. "Multi-level Multimodal
Common Semantic Space for Image-Phrase Grounding." CVPR 2019. [code]
Grounding with weak supervision
63. 63
Cornia, Marcella, Lorenzo Baraldi, and Rita Cucchiara. "Show, Control and Tell: A Framework for Generating Controllable and
Grounded Captions." CVPR 2019. [code]
Controlled Grounding
66. 66
Reed, Scott, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee. "Generative adversarial
text to image synthesis." ICML 2016.
Image Generation
67. 67
Reed, Scott, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee. "Generative adversarial
text to image synthesis." ICML 2016. [code]
Image Synthesis
68. 68
Reed, Scott, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee. "Generative adversarial
text to image synthesis." ICML 2016. [code]
Image Generation
69. 69
#StackGAN Zhang, Han, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaolei Huang, Xiaogang Wang, and Dimitris Metaxas.
"Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks." ICCV 2017. [code]
Image Synthesis
70. 70
#StackGAN Zhang, Han, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaolei Huang, Xiaogang Wang, and Dimitris Metaxas.
"Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks." ICCV 2017. [code]
Image Synthesis
71. 71Justin Johnson, Agrim Gupta, Li Fei-Fei, “Image Generation from Scene Graphs” CVPR 2018
Image Generation via Scene Graphs
72. 72Justin Johnson, Agrim Gupta, Li Fei-Fei, “Image Generation from Scene Graphs” CVPR 2018
Image Synthesis via Scene Graphs
73. 73
#Text2Scene Tan, Fuwen, Song Feng, and Vicente Ordonez. "Text2Scene: Generating Compositional Scenes From Textual
Descriptions." CVPR 2019 [blog].
Image Generation by Composition
74. 74
#Text2Scene Tan, Fuwen, Song Feng, and Vicente Ordonez. "Text2Scene: Generating Compositional Scenes From Textual
Descriptions." CVPR 2019 [blog].
75. 75
#Text2Scene Tan, Fuwen, Song Feng, and Vicente Ordonez. "Text2Scene: Generating Compositional Scenes From Textual
Descriptions." CVPR 2019 [blog].
76. 76
#CRAFT Gupta, Tanmay, Dustin Schwenk, Ali Farhadi, Derek Hoiem, and Aniruddha Kembhavi. "Imagine this! scripts to
compositions to videos." ECCV 2018
77. 77
#CRAFT Gupta, Tanmay, Dustin Schwenk, Ali Farhadi, Derek Hoiem, and Aniruddha Kembhavi. "Imagine this! scripts to
compositions to videos." ECCV 2018
Video Generation by Composition
80. 80
#Mattnet Yu, Licheng, Zhe Lin, Xiaohui Shen, Jimei Yang, Xin Lu, Mohit Bansal, and Tamara L. Berg. "Mattnet: Modular
attention network for referring expression comprehension." CVPR 2018. [code]
Object from Referring Expressions
81. 81
Khoreva, Anna, Anna Rohrbach, and Bernt Schiele. "Video object segmentation with language referring expressions." ACCV
2018.
Video Object Grounding
86. 86
Visual Question Answering (VQA)
Masuda, Issey, Santiago Pascual de la Puente, and Xavier Giro-i-Nieto. "Open-Ended Visual
Question-Answering." ETSETB UPC TelecomBCN (2016).
Image
Question
Answer
87. 87
Visual Question Answering (VQA)
Francisco Roldán, Issey Masuda, Santiago Pascual de la Puente, and Xavier Giro-i-Nieto. "Visual
Question-Answering 2.0." ETSETB UPC TelecomBCN (2017).
88. 88
Noh, H., Seo, P. H., & Han, B. Image question answering using convolutional neural network with
dynamic parameter prediction. CVPR 2016
Dynamic Parameter Prediction Network (DPPnet)
Visual Question Answering (VQA)
89. 89
VQA: Dynamic Memory Networks
(Slides and Slidecast by Santi Pascual): Xiong, Caiming, Stephen Merity, and Richard Socher. "Dynamic Memory Networks for
Visual and Textual Question Answering." ICML 2016
90. 90
Grounded VQA
(Slides and Screencast by Issey Masuda): Zhu, Yuke, Oliver Groth, Michael Bernstein, and Li Fei-Fei."Visual7W: Grounded
Question Answering in Images." CVPR 2016.
91. 91
Visual Reasoning
#Clevr Johnson, Justin, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, and Ross Girshick.
"CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning." CVPR 2017
92. 92
Visual Reasoning
(Slides by Fran Roldan) Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Fei-Fei Li, Larry
Zitnick, Ross Girshick , “Inferring and Executing Programs for Visual Reasoning”. ICCV 2017
Program Generator Execution Engine
93. 93
Visual Dialog
Das, Abhishek, Satwik Kottur, Khushi Gupta, Avi Singh, Deshraj Yadav, José MF Moura, Devi Parikh, and Dhruv Batra. "Visual
Dialog." CVPR 2017 [Project]
95. 95
Hate Speech Detection in Memes
Benet Oriol, Cristian Canton, Xavier Giro-i-Nieto, “Hate Speech Detection in Memes”. UPC TelecomBCN
2019.
Hate Speech Detection
96. 96
Visual Reasoning: Relation Networks
Santoro, Adam, David Raposo, David G. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Timothy
Lillicrap. "A simple neural network module for relational reasoning." NIPS 2017.
Relation Networks concatenate all possible pairs of objects with the an encoded question to later find the
answer with a MLP.
100. 100
Joint Representations (Embeddings)
Frome, Andrea, Greg S. Corrado, Jon Shlens, Samy Bengio, Jeff Dean, and Tomas Mikolov. "Devise: A deep
visual-semantic embedding model." NIPS 2013
101. 101
Zero-shot learning
Socher, R., Ganjoo, M., Manning, C. D., & Ng, A., Zero-shot learning through cross-modal transfer. NIPS 2013 [slides] [code]
No images from “cat” in
the training set...
...but they can still be
recognised as “cats”
thanks to the
representations learned
from text .
102. 102
Multimodal Retrieval
Aytar, Yusuf, Lluis Castrejon, Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba. "Cross-Modal Scene Networks."
CVPR 2016.
103. 103
Multimodal Retrieval
Aytar, Yusuf, Lluis Castrejon, Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba. "Cross-Modal Scene Networks."
CVPR 2016.
104. 104
Image and text retrieval with joint embeddings.
Joint Neural Embeddings
#pic2recipe Amaia Salvador, Nicholas Haynes, Yusuf Aytar, Javier Marín, Ferda Ofli, Ingmar Weber, Antonio
Torralba, “Learning Cross-modal Embeddings for Cooking Recipes and Food Images”. CVPR 2017
105. 105
#pic2recipe Amaia Salvador, Nicholas Haynes, Yusuf Aytar, Javier Marín, Ferda Ofli, Ingmar Weber, Antonio
Torralba, “Learning Cross-modal Embeddings for Cooking Recipes and Food Images”. CVPR 2017
Joint Neural Embeddings
106. 106
Joint Neural Embeddings
joint
embedding
LSTM Bidirectional LSTM
#pic2recipe Amaia Salvador, Nicholas Haynes, Yusuf Aytar, Javier Marín, Ferda Ofli, Ingmar Weber, Antonio
Torralba, “Learning Cross-modal Embeddings for Cooking Recipes and Food Images”. CVPR 2017
107. 107
Joint Neural Embeddings
● Constrained to database recipes
● Ingredients and Instructions are retrieved as a whole
● Prohibits user manipulation (ingredient replacements)
#pic2recipe Amaia Salvador, Nicholas Haynes, Yusuf Aytar, Javier Marín, Ferda Ofli, Ingmar Weber, Antonio
Torralba, “Learning Cross-modal Embeddings for Cooking Recipes and Food Images”. CVPR 2017
109. 109
Recipe Generation (not retrieval !)
Salvador, Amaia, Michal Drozdzal, Xavier Giro-i-Nieto, and Adriana Romero. "Inverse Cooking: Recipe
Generation from Food Images." CVPR 2019.
110. 110
Recipe Generation (not retrieval !)
Salvador, Amaia, Michal Drozdzal, Xavier Giro-i-Nieto, and Adriana Romero. "Inverse Cooking: Recipe
Generation from Food Images." CVPR 2019.
Title: Edamame corn salad
Ingredients
pepper, corn, onion, edamame, salt, vinegar, cilantro, avocado, oil
Instructions
- In a large bowl, combine edamame, corn, red onion, cilantro,
avocado, and red bell pepper.
- In a small bowl, whisk together olive oil, vinegar, salt, and
pepper.
- Pour dressing over edamame mixture and toss to coat.
- Cover and refrigerate for at least 1 hour before serving.
111. 111
Recipe Generation (not retrieval !)
Salvador, Amaia, Michal Drozdzal, Xavier Giro-i-Nieto, and Adriana Romero. "Inverse Cooking: Recipe
Generation from Food Images." CVPR 2019.
According to human judgment, our proposed system is able to generate better recipes than the previous
retrieval method.
112. 112
Recipe Generation (data as the DL ingredient!)
Salvador, Amaia, Michal Drozdzal, Xavier Giro-i-Nieto, and Adriana Romero. "Inverse Cooking: Recipe
Generation from Food Images." CVPR 2019.
Title: Spaghetti with spicy tomato sauce
Ingredients:
onion, tomato, chili, salt, noodles, pepper, spaghetti, clove, cumin, water
Instructions:
-In a large pot, combine the tomatoes, onion, garlic, chili powder, cumin, salt,
pepper, water and tomato sauce.
-Bring to a boil, then reduce heat and simmer for about 20 minutes.
-Meanwhile, cook the spaghetti according to package directions.
-Drain and set aside.
-When the spaghetti is done, drain and return to pot.
-Add the sauce and stir to combine.
-Serve with the shredded cheese and a dollop of sour cream.
122. Rowan Zellers, Yonatan Bisk, Ali Farhadi, Yejin Choi. From Recognition to Cognition: Visual Commonsense
Reasoning. CVPR 2019 (oral)
https://visualcommonsense.com/
123. 123
Ma, Chih-Yao, Jiasen Lu, Zuxuan Wu, Ghassan AlRegib, Zsolt Kira, Richard Socher, and Caiming Xiong.
"Self-Monitoring Navigation Agent via Auxiliary Progress Estimation." ICLR 2019. [code]
124. 124
Visual Question Answering
Gurari, Danna, Qing Li, Abigale J. Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, and Jeffrey P. Bigham. "VizWiz
Grand Challenge: Answering Visual Questions from Blind People." arXiv preprint arXiv:1802.08218 (2018).
125. 125
Reasoning: MAC
Hudson, Drew A., and Christopher D. Manning. "Compositional attention networks for machine reasoning."
ICLR 2018.
126. 126
Navigation with Language and Vision
Fried, Daniel, Ronghang Hu, Volkan Cirik, Anna Rohrbach, Jacob Andreas, Louis-Philippe Morency, Taylor Berg-Kirkpatrick, Kate
Saenko, Dan Klein, and Trevor Darrell. "Speaker-Follower Models for Vision-and-Language Navigation." arXiv preprint
arXiv:1806.02724 (2018).
127. 127
Translation
Harwath, David, Galen Chuang, and James Glass. "Vision as an Interlingua: Learning Multilingual Semantic
Embeddings of Untranscribed Speech." arXiv preprint arXiv:1804.03052 (2018).