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Relative attributes
Slides by Delia Fernandez at the the UPC Computer Vision Reading Group about the paper:
Parikh, Devi, and Kristen Grauman. "Relative attributes." In Computer Vision (ICCV), 2011 IEEE International Conference on, pp. 503-510. IEEE, 2011.
Abstract:
Human-nameable visual “attributes” can benefit various recognition tasks. However, existing techniques restrict these properties to categorical labels (for example, a person is `smiling' or not, a scene is `dry' or not), and thus fail to capture more general semantic relationships. We propose to model relative attributes. Given training data stating how object/scene categories relate according to different attributes, we learn a ranking function per attribute. The learned ranking functions predict the relative strength of each property in novel images. We then build a generative model over the joint space of attribute ranking outputs, and propose a novel form of zero-shot learning in which the supervisor relates the unseen object category to previously seen objects via attributes (for example, `bears are furrier than giraffes'). We further show how the proposed relative attributes enable richer textual descriptions for new images, which in practice are more precise for human interpretation. We demonstrate the approach on datasets of faces and natural scenes, and show its clear advantages over traditional binary attribute prediction for these new tasks.
Photo Clustering of Social Events by Extending PhotoTOC to a Rich Context
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http://mklab2.iti.gr/sewm14/
The popularisation of the storage of photos on the cloud has opened new opportunities and challenges for the organisa- tion and extension of photo collections. This paper presents a light computational solution for the clustering of web pho- tos based on social events. The proposal combines a first over-segmentation of the photo collections of each user based on temporal cues, as previously proposed in PhotoTOC. On a second stage, the resulting mini-clusters are merged based on contextual metadata such as geolocation, keywords and user IDs. Results indicate that, although temporal cues are very relevant for event clustering, robust solutions should also consider all these additional features.Bundling interest points for object classification
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Crowdsourced Object Segmentation with a Game
We introduce a new algorithm for image segmentation based on crowdsourcing through a game : Ask'nSeek. The game provides information on the objects of an image, under
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https://imatge.upc.edu/web/publications/crowdsourced-object-segmentation-game-0
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Master thesis at Dublin City University, September 2013.
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More details:
https://imatge.upc.edu/web/publications/investigating-eeg-saliency-and-segmentation-applications-image-processing Describing videos by exploiting temporal structure
Slides from the UPC Computer Vision reading Group.
https://github.com/imatge-upc/readcv
...on the original paper:
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Listen to the audio: https://www.youtube.com/watch?v=sM5pxgmAIiA
ABSTRACT
Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are static, working with videos requires modeling their dynamic temporal structure and then properly integrating that information into a natural language description model. In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions. First, our approach incorporates a spatial temporal 3-D convolutional neural network (3-D CNN) representation of the short temporal dynamics. The 3-D CNN representation is trained on video action recognition tasks, so as to produce a representation that is tuned to human motion and behavior. Second we propose a temporal attention mechanism that allows to go beyond local temporal modeling and learns to automatically select the most relevant temporal segments given the text-generating RNN. Our approach exceeds the current state-of-art for both BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on a new, larger and more challenging dataset of paired video and natural language descriptions.
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Outreach talk addressed to high school students. It presents computer vision and artificial intelligence as an emerging field with high potential in the future years. It also highlights Universitat Politecnica de Catalunya as a top college to study related topics.
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Slides by Delia Fernandez at the the UPC Computer Vision Reading Group about the paper:
Parikh, Devi, and Kristen Grauman. "Relative attributes." In Computer Vision (ICCV), 2011 IEEE International Conference on, pp. 503-510. IEEE, 2011.
Abstract:
Human-nameable visual “attributes” can benefit various recognition tasks. However, existing techniques restrict these properties to categorical labels (for example, a person is `smiling' or not, a scene is `dry' or not), and thus fail to capture more general semantic relationships. We propose to model relative attributes. Given training data stating how object/scene categories relate according to different attributes, we learn a ranking function per attribute. The learned ranking functions predict the relative strength of each property in novel images. We then build a generative model over the joint space of attribute ranking outputs, and propose a novel form of zero-shot learning in which the supervisor relates the unseen object category to previously seen objects via attributes (for example, `bears are furrier than giraffes'). We further show how the proposed relative attributes enable richer textual descriptions for new images, which in practice are more precise for human interpretation. We demonstrate the approach on datasets of faces and natural scenes, and show its clear advantages over traditional binary attribute prediction for these new tasks.
Photo Clustering of Social Events by Extending PhotoTOC to a Rich Context
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Presented at ICMR 2014 Workshop on Social Events in Web Multimedia (SEWM).
http://mklab2.iti.gr/sewm14/
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Slides from the UPC Computer Vision reading Group.
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Yao, Li, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, and Aaron Courville. "Describing videos by exploiting temporal structure." In Proceedings of the IEEE International Conference on Computer Vision, pp. 4507-4515. 2015.
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Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are static, working with videos requires modeling their dynamic temporal structure and then properly integrating that information into a natural language description model. In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions. First, our approach incorporates a spatial temporal 3-D convolutional neural network (3-D CNN) representation of the short temporal dynamics. The 3-D CNN representation is trained on video action recognition tasks, so as to produce a representation that is tuned to human motion and behavior. Second we propose a temporal attention mechanism that allows to go beyond local temporal modeling and learns to automatically select the most relevant temporal segments given the text-generating RNN. Our approach exceeds the current state-of-art for both BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on a new, larger and more challenging dataset of paired video and natural language descriptions.
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Full details: https://imatge.upc.edu/web/publications/visual-instance-mining-news-videos-using-graph-based-approach
Author: David Almendros-Gutiérrez
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Ventura, C.; Giro-i-Nieto, X.; Vilaplana, V.; Giribet, D.; Carasusan, E., "Automatic keyframe selection based on mutual reinforcement algorithm," Content-Based Multimedia Indexing (CBMI), 2013 11th International Workshop on , vol., no., pp.29,34, 17-19 June 2013
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State-of-the-art object detection systems rely on an accurate set of region proposals. Several recent methods use a neural network architecture to hypothesize promising object locations. While these approaches are computationally efficient, they rely on fixed image regions as anchors for predictions. In this paper we propose to use a search strategy that adaptively directs computational resources to sub-regions likely to contain objects. Compared to methods based on fixed anchor locations, our approach naturally adapts to cases where object instances are sparse and small. Our approach is comparable in terms of accuracy to the state-of-the-art Faster R-CNN approach while using two orders of magnitude fewer anchors on average. Code is publicly available.
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Details: https://imatge.upc.edu/web/publications/rich-internet-application-semi-automatic-annotation-semantic-shots-keyframes
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Speaker: Manel Baradad
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https://github.com/imatge-upc/readcv/blob/master/README.md
Improving Spatial Codification in Semantic Segmentation
https://imatge.upc.edu/web/publications/improving-spatial-codification-semantic-segmentation
Ventura C, Giró-i-Nieto X, Vilaplana V, McGuinness K, Marqués F, O'Connor N. Improving Spatial Codification in Semantic Segmentation. In: IEEE International Conference on Image Processing (ICIP), 2015. Quebec City: IEEE; 2015
This paper explores novel approaches for improving the spatial codification for the pooling of local descriptors to solve the semantic segmentation problem. We propose to partition the image into three regions for each object to be described: Figure, Border and Ground. This partition aims at minimizing the influence of the image context on the object description and vice versa by introducing an intermediate zone around the object contour. Furthermore, we also propose a richer visual descriptor of the object by applying a Spatial Pyramid over the Figure region. Two novel Spatial Pyramid configurations are explored: Cartesian-based and crown-based Spatial Pyramids. We test these approaches with state-of-the-art techniques and show that they improve the Figure-Ground based pooling in the Pascal VOC 2011 and 2012 semantic segmentation challenges.
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More details in:
https://imatge.upc.edu/web/publications/part-based-object-retrieval-binary-partition-trees
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Slides by Issey Masuda about the paper:
Zhu, Yuke, Oliver Groth, Michael Bernstein, and Li Fei-Fei. "Visual7W: Grounded Question Answering in Images." CVPR 2016.
We have seen great progress in basic perceptual tasks such as object recognition and detection. However, AI models still fail to match humans in high-level vision tasks due to the lack of capacities for deeper reasoning. Recently the new task of visual question answering (QA) has been proposed to evaluate a model's capacity for deep image understanding. Previous works have established a loose, global association between QA sentences and images. However, many questions and answers, in practice, relate to local regions in the images. We establish a semantic link between textual descriptions and image regions by object-level grounding. It enables a new type of QA with visual answers, in addition to textual answers used in previous work. We study the visual QA tasks in a grounded setting with a large collection of 7W multiple-choice QA pairs. Furthermore, we evaluate human performance and several baseline models on the QA tasks. Finally, we propose a novel LSTM model with spatial attention to tackle the 7W QA tasks.
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https://imatge.upc.edu/web/publications/semantic-and-diverse-summarization-egocentric-photo-events
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Zhong, Yujie, Relja Arandjelović, and Andrew Zisserman. "Faces In Places: compound query retrieval." In BMVC 2016.
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http://imatge-upc.github.io/telecombcn-2016-dlcv/
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You can also check it in Sales Sharing Site "www.lunchntouch.com"
If you want a brochure in your language,
send E-mail below E-mail address unquestioningly!
(English, Chinese, Japanese, french, Portuguese, Spanish, etc)
Import & Export discussion Welcome, too!
Thank you for your touch!
E-mail address : yun@lunchntouch.com
"We can Make happy place for small business! Let's do it!!"
= Lunch N Touch =
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You can also check it in Sales Sharing Site "www.lunchntouch.com"
If you want a brochure in your language,
send E-mail below E-mail address unquestioningly!
(English, Chinese, Japanese, french, Portuguese, Spanish, etc)
Import & Export discussion Welcome, too!
Thank you for your touch!
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"We can Make happy place for small business! Let's do it!!"
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You can also check it in Sales Sharing Site "www.lunchntouch.com"
If you want a brochure in your language,
send E-mail below E-mail address unquestioningly!
(English, Chinese, Japanese, french, Portuguese, Spanish, etc)
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Thank you for your touch!
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"We can Make happy place for small business! Let's do it!!"
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You can also check it in Sales Sharing Site "www.lunchntouch.com"
If you want a brochure in your language,
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"We can Make happy place for small business! Let's do it!!"
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BSc thesis by Marcel Tella-Amo at the Telecommunications Engineering studies in the School of Engineering of Terrassa (EET), Universitat Politècnica de Cataluna (UPC).
Co-advised by Xavier Giró-i-Nieto and Albert Gil.
This diploma thesis aims to provide a framework for developing web applications for ImagePlus, the software develpment platform in C++ of the Image Processing Group of the Technical University of Catalonia (UPC). These web applications are to demonstrate the functionality of the image processing algorithms to any visitor to the group website. Developers are also benefited from this graphical user interface because they can easily create Graphical User Interfaces (GUIs) for the processing algorithms.
More information:
Visual instance mining of news videos using a graph-based approach
Visual instance mining of news videos using a graph-based approachUniversitat Politècnica de Catalunya
Full details: https://imatge.upc.edu/web/publications/visual-instance-mining-news-videos-using-graph-based-approach
Author: David Almendros-Gutiérrez
Advisors: Xavier Giró-i-Nieto (UPC) and Horst Eidenberger (TU Wien)
Degree: Telecommunications Engineering (5 years) at Telecom BCN-ETSETB (UPC)
The aim of this thesis is to design a tool that performs visual instance search mining for news video summarization. This means to extract the relevant content of the video in order to be able to recognize the storyline of the news.
Initially, a sampling of the video is required to get the frames with a desired rate. Then, different relevant contents are detected from each frame, focusing on faces, text and several objects that the user can select. Next, we use a graph-based clustering method in order to recognize them with a high accuracy and select the most representative ones to show them in the visual summary. Furthermore, a graphical user interface in Wt was developed to create an online demo to test the application.
During the development of the application we have been testing the tool with the CCMA dataset. We prepared a web-based survey based on four results from this dataset to check the opinion of the users. We also validate our visual instance mining results comparing them with the results obtained applying an algorithm developed at Columbia University for video summarization. We have run the algorithm on a dataset of a few videos on two events: 'Boston bombings' and the 'search of the Malaysian airlines flight'. We carried out another web-based survey in which users could compare our approach with this related work. With these surveys we analyze if our tool fulfill the requirements we set up.
We can conclude that our system extract visual instances that show the most relevant content of news videos and can be used to summarize these videos effectively.Where should saliency models look next ? (UPC Reading Group)
Slides by Junting Pan on the paper:
Bylinskii, Zoya, Adrià Recasens, Ali Borji, Aude Oliva, Antonio Torralba, and Frédo Durand. "Where Should Saliency Models Look Next?." In European Conference on Computer Vision, pp. 809-824. Springer International Publishing, 2016.
A Presentation During Vacation Term Activities At Afro Caribbean Achievement ...
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Automatic Keyframe Selection based on Mutual Reinforcement Algorithm
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Ventura, C.; Giro-i-Nieto, X.; Vilaplana, V.; Giribet, D.; Carasusan, E., "Automatic keyframe selection based on mutual reinforcement algorithm," Content-Based Multimedia Indexing (CBMI), 2013 11th International Workshop on , vol., no., pp.29,34, 17-19 June 2013
doi: 10.1109/CBMI.2013.6576548
This paper addresses the problem of video summarization through an automatic selection of a single representative keyframe. The proposed solution is based on the mutual reinforcement paradigm, where a keyframe is selected thanks to its highest and most frequent similarity to the rest of considered frames. Two variations of the algorithm are explored: a first one where only frames within the same video are used (intraclip mode) and a second one where the decision also depends on the previously selected keyframes of related videos (interclip mode). These two algorithms were evaluated by a set of
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More details:
https://imatge.upc.edu/web/publications/automatic-keyframe-selection-based-mutual-reinforcement-algorithmAdaptive object detection using adjacency and zoom prediction
Slides by Miriam Bellver from the Computer Vision Reading Group at the Universitat Politecnica de Catalunya about the paper:
Lu, Yongxi, Tara Javidi, and Svetlana Lazebnik. "Adaptive Object Detection Using Adjacency and Zoom Prediction." CVPR 2016
Abstract:
State-of-the-art object detection systems rely on an accurate set of region proposals. Several recent methods use a neural network architecture to hypothesize promising object locations. While these approaches are computationally efficient, they rely on fixed image regions as anchors for predictions. In this paper we propose to use a search strategy that adaptively directs computational resources to sub-regions likely to contain objects. Compared to methods based on fixed anchor locations, our approach naturally adapts to cases where object instances are sparse and small. Our approach is comparable in terms of accuracy to the state-of-the-art Faster R-CNN approach while using two orders of magnitude fewer anchors on average. Code is publicly available.
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Authors: Elisabet Carcel, Manuel Martos, Xavier Giró-i-Nieto and Ferran Marqués
Details: https://imatge.upc.edu/web/publications/rich-internet-application-semi-automatic-annotation-semantic-shots-keyframes
This paper describes a system developed for the semi- automatic annotation of keyframes in a broadcasting company. The tool aims at assisting archivists who traditionally label every keyframe manually by suggesting them an automatic annotation that they can intuitively edit and validate. The system is valid for any domain as it uses generic MPEG-7 visual descriptors and binary SVM classifiers. The classification engine has been tested on the multiclass problem of semantic shot detection, a type of metadata used in the company to index new con- tent ingested in the system. The detection performance has been tested in two different domains: soccer and parliament. The core engine is ac- cessed by a Rich Internet Application via a web service. The graphical user interface allows the edition of the suggested labels with an intuitive drag and drop mechanism between rows of thumbnails, each row representing a different semantic shot class. The system has been described as complete and easy to use by the professional archivists at the company. Recurrent Instance Segmentation (UPC Reading Group)
Speaker: Manel Baradad
Paper: Romera-Paredes, Bernardino, and Philip HS Torr. "Recurrent instance segmentation." ECCV 2016.
Reading group website:
https://github.com/imatge-upc/readcv/blob/master/README.md
Improving Spatial Codification in Semantic Segmentation
https://imatge.upc.edu/web/publications/improving-spatial-codification-semantic-segmentation
Ventura C, Giró-i-Nieto X, Vilaplana V, McGuinness K, Marqués F, O'Connor N. Improving Spatial Codification in Semantic Segmentation. In: IEEE International Conference on Image Processing (ICIP), 2015. Quebec City: IEEE; 2015
This paper explores novel approaches for improving the spatial codification for the pooling of local descriptors to solve the semantic segmentation problem. We propose to partition the image into three regions for each object to be described: Figure, Border and Ground. This partition aims at minimizing the influence of the image context on the object description and vice versa by introducing an intermediate zone around the object contour. Furthermore, we also propose a richer visual descriptor of the object by applying a Spatial Pyramid over the Figure region. Two novel Spatial Pyramid configurations are explored: Cartesian-based and crown-based Spatial Pyramids. We test these approaches with state-of-the-art techniques and show that they improve the Figure-Ground based pooling in the Pascal VOC 2011 and 2012 semantic segmentation challenges.
Part-based Object Retrieval with Binary Partition Trees
Phd thesis co-advised by Ferran Marqués (UPC) and Shih-Fu Chang (Columbia University).
More details in:
https://imatge.upc.edu/web/publications/part-based-object-retrieval-binary-partition-trees
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Slides by Issey Masuda about the paper:
Zhu, Yuke, Oliver Groth, Michael Bernstein, and Li Fei-Fei. "Visual7W: Grounded Question Answering in Images." CVPR 2016.
We have seen great progress in basic perceptual tasks such as object recognition and detection. However, AI models still fail to match humans in high-level vision tasks due to the lack of capacities for deeper reasoning. Recently the new task of visual question answering (QA) has been proposed to evaluate a model's capacity for deep image understanding. Previous works have established a loose, global association between QA sentences and images. However, many questions and answers, in practice, relate to local regions in the images. We establish a semantic link between textual descriptions and image regions by object-level grounding. It enables a new type of QA with visual answers, in addition to textual answers used in previous work. We study the visual QA tasks in a grounded setting with a large collection of 7W multiple-choice QA pairs. Furthermore, we evaluate human performance and several baseline models on the QA tasks. Finally, we propose a novel LSTM model with spatial attention to tackle the 7W QA tasks.
Semantic and Diverse Summarization of Egocentric Photo Events
https://imatge.upc.edu/web/publications/semantic-and-diverse-summarization-egocentric-photo-events
This project generates visual summaries of events depicted from egocentric photos taken with a wearable camera. These summaries are addressed to mild-dementia patients in order to exercise their memory in a daily base. The main contribution is an iterative approach that guarantees the semantic diversity of the summary and a novel soft metric to assess subjective results. Medical experts validated the proposed solution with a Mean Opinion Score of 4.6 out of of 5.0. The flexibility and quality of the solution was also tested in the 2015 Retrieving Diverse Social Images Task from the scientific international benchmark, MediaEval.
Faces in Places: Compound Query Retrieval
Slides from Eva Mohedano and Andrea Calafell for the the UPC Computer Vision Reading Group about the paper:
Zhong, Yujie, Relja Arandjelović, and Andrew Zisserman. "Faces In Places: compound query retrieval." In BMVC 2016.
The goal of this work is to retrieve images containing both a target person and a target scene type from a large dataset of images. At run time this compound query is handled using a face classifier trained for the person, and an image classifier trained for the scene type. We make three contributions: first, we propose a hybrid convolutional neural network architecture that produces place-descriptors that are aware of faces and their corresponding descriptors. The network is trained to correctly classify a combination of face and scene classifier scores. Second, we propose an image synthesis system to render high quality fully-labelled face-and-place images, and train the network only from these synthetic images. Last, but not least, we collect and annotate a dataset of real images containing celebrities in different places, and use this dataset to evaluate the retrieval system. We demonstrate significantly improved retrieval performance for compound queries using the new face-aware place-descriptors.
Deep Learning for Computer Vision: Unsupervised Learning (UPC 2016)
Deep Learning for Computer Vision: Unsupervised Learning (UPC 2016)Universitat Politècnica de Catalunya
http://imatge-upc.github.io/telecombcn-2016-dlcv/
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 text captioning.Viewers also liked (16)
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