Master thesis defence by Manuel Martos-Asensio
Advisors: Horst Eidenberger (Technische Universtität Viena) and Xavier Giró-i-Nieto (Universitat Politècnica de Catalunya)
More details
https://imatge.upc.edu/web/publications/keyframe-based-video-summarization-designer
This Final Degree Work extends two previous projects and consists in carrying out an improvement of the video keyframe extraction module from one of them called Designer Master, by integrating the algorithms that were developed in the other, Object Maps.
Firstly the proposed solution is explained, which consists in a shot detection method, where the input video is sampled uniformly and afterwards, cumulative pixel-to-pixel difference is applied and a classifier decides which frames are keyframes or not.
Last, to validate our approach we conducted a user study in which both applications were compared. Users were asked to complete a survey regarding to different summaries created by means of the original application and with the one developed in this project. The results obtained were analyzed and they showed that the improvement done in the keyframes extraction module improves slightly the application performance and the quality of the generated summaries.
Yinyin Liu presents a model for object detection and localization, called Fast-RCNN. She will show how to introduce a ROI pooling layer into neon, and how to add the PASCAL VOC dataset to interface with model training and inference. Lastly, Yinyin will run through a demo on how to apply the trained model to detect new objects.
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://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://imatge.upc.edu/web/publications/keyframe-based-video-summarization-designer
This Final Degree Work extends two previous projects and consists in carrying out an improvement of the video keyframe extraction module from one of them called Designer Master, by integrating the algorithms that were developed in the other, Object Maps.
Firstly the proposed solution is explained, which consists in a shot detection method, where the input video is sampled uniformly and afterwards, cumulative pixel-to-pixel difference is applied and a classifier decides which frames are keyframes or not.
Last, to validate our approach we conducted a user study in which both applications were compared. Users were asked to complete a survey regarding to different summaries created by means of the original application and with the one developed in this project. The results obtained were analyzed and they showed that the improvement done in the keyframes extraction module improves slightly the application performance and the quality of the generated summaries.
Yinyin Liu presents a model for object detection and localization, called Fast-RCNN. She will show how to introduce a ROI pooling layer into neon, and how to add the PASCAL VOC dataset to interface with model training and inference. Lastly, Yinyin will run through a demo on how to apply the trained model to detect new objects.
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://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://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.
Operation-wise Attention Network for Tampering Localization Fusion.Weverify
In this work, we present a deep learning-based approach for image tampering localization fusion. This approach is designed to combine the outcomes of multiple image forensics algorithms and provides a fused tampering localization map, which requires no expert knowledge and is easier to interpret by end users. Our fusion framework includes a set of five individual tampering localization methods for splicing localization on JPEG images. The proposed deep learning fusion model is an adapted architecture, initially proposed for the image restoration task, that performs multiple operations in parallel, weighted by an attention mechanism to enable the selection of proper operations depending on the input signals. This weighting process can be very beneficial for cases where the input signal is very diverse, as in our case where the output signals of multiple image forensics algorithms are combined. Evaluation in three publicly available forensics datasets demonstrates that the performance of the proposed approach is competitive, outperforming the individual forensics techniques as well as another recently proposed fusion framework in the majority of cases.
Object detection is an important computer vision technique with applications in several domains such as autonomous driving, personal and industrial robotics. The below slides cover the history of object detection from before deep learning until recent research. The slides aim to cover the history and future directions of object detection, as well as some guidelines for how to choose which type of object detector to use for your own project.
A Comparison of People Counting Techniques viaVideo Scene AnalysisPoo Kuan Hoong
Real-time human detection and tracking from video surveillance footages is one of the most active research areas in computer vision and pattern recognition. This is due to the widespread application from being able to do it well. One such application is the counting of people, or density estimation, where the two key components are human detection and tracking. Traditional methods such as the usage of sensors are not suitable as they are not easily integrated with current video surveillance systems. As video surveillance systems are currently prevalent in most places, using vision based people counting techniques will be the logical approach. In this paper, we compared the two commonly used techniques which are Cascade Classifier and Histograms of Gradients (HOG) for human detection. We evaluated and compared these two techniques with three different video datasets with three different setting characteristics. From our experiment results, both Cascade Classifier and HOG techniques can be used for people counting to achieve moderate accuracy results.
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://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://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.
Fast Non-Uniform Filtering with Symmetric Weighted Integral Imagesdavidmarimon
Oral presentation at IEEE International Conference on Image Processing (ICIP), Hong Kong, September 2010.
Abstract: Non-uniform filters are frequently used in many image processing applications to describe regions or to detect specific features. However, non-uniform filtering is a computationally complex task. This paper presents a method to perform fast non-uniform filtering using a reduced number of memory accesses. The idea is based on integral images which are commonly used for box or Haar wavelet filtering. The disadvantage of those filters for several applications is their uniform shape. We describe a method to build Symmetric Weighted Integral Images that are tailored for a variety of kernels and the process to perform fast filtering with them. We show a relevant speedup when compared to Kernel Integral Images and large when compared to conventional non-uniform filtering by reducing the computational complexity.
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.
Operation-wise Attention Network for Tampering Localization Fusion.Weverify
In this work, we present a deep learning-based approach for image tampering localization fusion. This approach is designed to combine the outcomes of multiple image forensics algorithms and provides a fused tampering localization map, which requires no expert knowledge and is easier to interpret by end users. Our fusion framework includes a set of five individual tampering localization methods for splicing localization on JPEG images. The proposed deep learning fusion model is an adapted architecture, initially proposed for the image restoration task, that performs multiple operations in parallel, weighted by an attention mechanism to enable the selection of proper operations depending on the input signals. This weighting process can be very beneficial for cases where the input signal is very diverse, as in our case where the output signals of multiple image forensics algorithms are combined. Evaluation in three publicly available forensics datasets demonstrates that the performance of the proposed approach is competitive, outperforming the individual forensics techniques as well as another recently proposed fusion framework in the majority of cases.
Object detection is an important computer vision technique with applications in several domains such as autonomous driving, personal and industrial robotics. The below slides cover the history of object detection from before deep learning until recent research. The slides aim to cover the history and future directions of object detection, as well as some guidelines for how to choose which type of object detector to use for your own project.
A Comparison of People Counting Techniques viaVideo Scene AnalysisPoo Kuan Hoong
Real-time human detection and tracking from video surveillance footages is one of the most active research areas in computer vision and pattern recognition. This is due to the widespread application from being able to do it well. One such application is the counting of people, or density estimation, where the two key components are human detection and tracking. Traditional methods such as the usage of sensors are not suitable as they are not easily integrated with current video surveillance systems. As video surveillance systems are currently prevalent in most places, using vision based people counting techniques will be the logical approach. In this paper, we compared the two commonly used techniques which are Cascade Classifier and Histograms of Gradients (HOG) for human detection. We evaluated and compared these two techniques with three different video datasets with three different setting characteristics. From our experiment results, both Cascade Classifier and HOG techniques can be used for people counting to achieve moderate accuracy results.
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://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://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.
Fast Non-Uniform Filtering with Symmetric Weighted Integral Imagesdavidmarimon
Oral presentation at IEEE International Conference on Image Processing (ICIP), Hong Kong, September 2010.
Abstract: Non-uniform filters are frequently used in many image processing applications to describe regions or to detect specific features. However, non-uniform filtering is a computationally complex task. This paper presents a method to perform fast non-uniform filtering using a reduced number of memory accesses. The idea is based on integral images which are commonly used for box or Haar wavelet filtering. The disadvantage of those filters for several applications is their uniform shape. We describe a method to build Symmetric Weighted Integral Images that are tailored for a variety of kernels and the process to perform fast filtering with them. We show a relevant speedup when compared to Kernel Integral Images and large when compared to conventional non-uniform filtering by reducing the computational complexity.
VIDEO SUMMARIZATION: CORRELATION FOR SUMMARIZATION AND SUBTRACTION FOR RARE E...Journal For Research
The ever increasing number of surveillance camera networks being deployed all over the world has not only resulted in a high interest in the development of algorithms to automatically analyze the video footage, but has also opened new questions as how to efficiently manage the vast amount of information generated. The user may not have sufficient time to watch the entire video or the whole of video content may not be of interest to the user. In such cases, the user may just want to view the summary of the video instead of watching the whole video. In this paper, we present a video summarization technique developed in order to efficiently access the points of interest in the video footage. The technique aims to eliminate the sequences which contain no activity of significance. The system being developed actually captures each frame from the video, then it processes the frame; if the frame is of its interest, it retains the frames otherwise it discards the frame; hence the resultant video is very short. The proposed method is extended to obtain rare event detection for security systems. These rare event detections refer to suspicious scenarios. The system will consider a particular frame of interest from a video footage taken at given time and search for actions from video footages across the particular area of interest specified by the user. The user is then notified about the objects and actions occurred in the area of interest. This helps in detecting suspicious behavior that would have otherwise been deemed unsuspicious and gone unnoticed in the context of a narrow timeframe.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/dec-2016-member-meeting-uofw
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Professor Jeff Bilmes of the University of Washington delivers the presentation "Image and Video Summarization" at the December 2016 Embedded Vision Alliance Member Meeting. Bilmes provides an overview of the state of the art in image and video summarization.
ISO/IEC 18520 and xDR (PDR & VDR) ChallengeKurata Takeshi
(1) Progress on Benchmarking framework of vision-based spatial registration and tracking methods for mixed and augmented reality (MAR) (ISO/IEC 18520)
(2) xDR (PDR & VDR) Challenge: Survey on indoor localization competitions and benchmarking activities
Event-Handling Based Smart Video Surveillance SystemCSCJournals
a broad range of applications. Moving object classification in the field of video surveillance is a key component of smart surveillance software. In this paper, we have proposed reliable software with its large features for people, vehicle and object classification which works well in challenging real-world constraints, including the presence of shadows, low resolution imagery, occlusion, perspective distortions, arbitrary camera viewpoints, and groups of people. We have discussed a generic model of smart video surveillance systems that can meet requirements of strong commercial applications and also shown the implication of the software for the security purposes which made the whole system as a smart network. Smart surveillance systems use automatic image understanding techniques to extract information from the surveillance data.
Comparison of Matrix Completion Algorithms for Background Initialization in V...ActiveEon
Scene Background Modeling and Initialization (SBMI) Workshop in conjunction with ICIAP 2015.
Comparison of Matrix Completion Algorithms for Background Initialization in Videos
Recognition and tracking moving objects using moving camera in complex scenesIJCSEA Journal
In this paper, we propose a method for effectively tracking moving objects in videos captured using a
moving camera in complex scenes. The video sequences may contain highly dynamic backgrounds and
illumination changes. Four main steps are involved in the proposed method. First, the video is stabilized
using affine transformation. Second, intelligent selection of frames is performed in order to extract only
those frames that have a considerable change in content. This step reduces complexity and computational
time. Third, the moving object is tracked using Kalman filter and Gaussian mixture model. Finally object
recognition using Bag of features is performed in order to recognize the moving objects.
MIC-TJU at MediaEval Violent Scenes Detection (VSD) 2014multimediaeval
The task of Violent Scenes Detection requires creating a system to detect segments which contain physical violence in both movies and videos found on the web, which is a very challenging task due to camera jitters in hand-shot videos and free shot boundary in movies and web videos. In this paper, we present a novel system by combining shot boundary detection, feature extraction in both audio and video domains, Bag-of-Words model and Support Vector Machine. The key part of system lies in trajectory based features that are calculated around robust optical ows. These ows are extracted by a novel salient keypoint trajectory algorithm. According to our results, a good detection performance can be achieved by using trajectory based features combined with dense SIFT and MFCC.
http://ceur-ws.org/Vol-1263/mediaeval2014_submission_56.pdf
Automatic Foreground object detection using Visual and Motion SaliencyIJERD Editor
This paper presents a saliency-based video object extraction (VOE) framework. The proposed framework aims to automatically extract foreground objects of interest without any user interaction or the use of any training data (i.e., not limited to any particular type of object). To separate foreground and background regions within and across video frames, the proposed method utilizes visual and motion saliency information extracted from the input video. A conditional random field is applied to effectively combine the saliency induced features, which allows us to deal with unknown pose and scale variations of the foreground object (and its articulated parts). Based on the ability to preserve both spatial continuity and temporal consistency in the proposed VOE framework, experiments on a variety of videos verify that our method is able to produce quantitatively and qualitatively satisfactory VOE results.
Semantic Concept Detection in Video Using Hybrid Model of CNN and SVM Classif...CSCJournals
In today's era of digitization and fast internet, many video are uploaded on websites, a mechanism is required to access this video accurately and efficiently. Semantic concept detection achieve this task accurately and is used in many application like multimedia annotation, video summarization, annotation, indexing and retrieval. Video retrieval based on semantic concept is efficient and challenging research area. Semantic concept detection bridges the semantic gap between low level extraction of features from key-frame or shot of video and high level interpretation of the same as semantics. Semantic Concept detection automatically assigns labels to video from predefined vocabulary. This task is considered as supervised machine learning problem. Support vector machine (SVM) emerged as default classifier choice for this task. But recently Deep Convolutional Neural Network (CNN) has shown exceptional performance in this area. CNN requires large dataset for training. In this paper, we present framework for semantic concept detection using hybrid model of SVM and CNN. Global features like color moment, HSV histogram, wavelet transform, grey level co-occurrence matrix and edge orientation histogram are selected as low level features extracted from annotated groundtruth video dataset of TRECVID. In second pipeline, deep features are extracted using pretrained CNN. Dataset is partitioned in three segments to deal with data imbalance issue. Two classifiers are separately trained on all segments and fusion of scores is performed to detect the concepts in test dataset. The system performance is evaluated using Mean Average Precision for multi-label dataset. The performance of the proposed framework using hybrid model of SVM and CNN is comparable to existing approaches.
Video Key-Frame Extraction using Unsupervised Clustering and Mutual ComparisonCSCJournals
Key-frame extraction is one of the important steps in semantic concept based video indexing and retrieval and accuracy of video concept detection highly depends on the effectiveness of keyframe extraction method. Therefore, extracting key-frames efficiently and effectively from video shots is considered to be a very challenging research problem in video retrieval systems. One of many approaches to extract key-frames from a shot is to make use of unsupervised clustering. Depending on the salient content of the shot and results of clustering, key-frames can be extracted. But usually, because of the visual complexity and/or the content of the video shot, we tend to get near duplicate or repetitive key-frames having the same semantic content in the output and hence accuracy of key-frame extraction decreases. In an attempt to improve accuracy, we proposed a novel key-frame extraction method based on unsupervised clustering and mutual comparison where we assigned 70% weightage to color component (HSV histogram) and 30% to texture (GLCM), while computing a combined frame similarity index used for clustering. We suggested a mutual comparison of the key-frames extracted from the output of the clustering where each key-frame is compared with every other to remove near duplicate keyframes. The proposed algorithm is both computationally simple and able to detect non-redundant and unique key-frames for the shot and as a result improving concept detection rate. The efficiency and effectiveness are validated by open database videos.
Long-term Face Tracking in the Wild using Deep LearningElaheh Rashedi
This paper investigates long-term face tracking of a specific person given his/her face image in a single frame as a query in a video stream. Through taking advantage of pre-trained deep learning models on big data, a novel system is developed for accurate video face tracking in the unconstrained environments depicting various people and objects moving in and out of the frame. In the proposed system, we present a detection-verification-tracking method (dubbed as 'DVT') which accomplishes the long-term face tracking task through the collaboration of face detection, face verification, and (short-term) face tracking. An offline trained detector based on cascaded convolutional neural networks localizes all faces appeared in the frames, and an offline trained face verifier based on deep convolutional neural networks and similarity metric learning decides if any face or which face corresponds to the queried person. An online trained tracker follows the face from frame to frame. When validated on a sitcom episode and a TV show, the DVT method outperforms tracking-learning-detection (TLD) and face-TLD in terms of recall and precision. The proposed system is also tested on many other types of videos and shows very promising results.
Overview Of Video Object Tracking SystemEditor IJMTER
The goal of video object tracking system is segmenting a region of interest from a video
scene and keeping track of its motion, positioning and occlusion. There are the three steps of video
object tracking system those are object detection, object classification and object tracking. Object
detection is performed to check existence of objects in video. Then the detected object can be
classified in various categories on the basis on their shape, motion, color and texture. Object tracking
is performed using monitoring object changes. This paper we are going to take overview of different
object detection, object classification and object tracking techniques and also the comparison of
different techniques used for various stages of tracking.
These slides discuss some milestone results in image classification using Deep Convolutional neural network and talks about our results on Obscenity detection in images by using Deep Convolutional neural network and transfer learning on ImageNet models.
Similar to Content based video summarization into object maps (20)
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.
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.
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.
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.
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://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.
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).
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.
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.
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21. Content selection (VI)
Face clustering
Which faces belong to the same person?
Which faces appear more often in the video?
Unsupervised Face Clustering problem:
1. Unknown number of characters
2. Unknown ground truth
Solution:
Iterative cluster estimation using LBPH
26. Content selection (XI)
Object detection
Relevant content is related to source video
Custom object map with:
1. Haar cascades
2. SURF descriptors matching
3. Deformable parts models
27. Content selection (XII)
Object detection
Haar cascade classifiers
Advantages:
- Quick object detection
- Training and detection stages included in OpenCV
Disadvantages:
- Fails at giving good results with different object views
- Slow training process
28. Content selection (XIII)
Object detection
SURF descriptors matching
Advantages:
- No additional training stage needed
- Scale and rotation invariant method
- Real-time object detection
- Descriptors extraction and matching strategy included in OpenCV
Disadvantages:
- Very specific training image
- Object may not be located in the image
29. Content selection (XIV)
Object detection
Deformable parts models
Advantages:
- Multiple object views detection
- Scored results
Disadvantages:
- Third party executable wrapped in Java
- Slow object detection process
30. Contents
System overview
Requirements analysis
Solution approach
Preparation
Content selection
Compositing
Conclusions
Experimental results
Further work
42. Conclusions (III)
Content-based video summarization application
Customizable
Allows to rapidly grasp video content
Generates a summary description file to include related metadata
ACM 2013 Open Source Software Competition
Code publicly available at Sourceforge
http://sourceforge.net/p/objectmaps
43. Conclusions (VI)
Further work
Face clustering improvement
Audio content analysis and understanding
Video sequence analysis
Content presentation analysis
Social Media