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Author (down) Saiping Zhang, Luis Herranz, Marta Mrak, Marc Gorriz Blanch, Shuai Wan, Fuzheng Yang
Title PeQuENet: Perceptual Quality Enhancement of Compressed Video with Adaptation-and Attention-based Network Type Miscellaneous
Year 2022 Publication Arxiv Abbreviated Journal
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Abstract In this paper we propose a generative adversarial network (GAN) framework to enhance the perceptual quality of compressed videos. Our framework includes attention and adaptation to different quantization parameters (QPs) in a single model. The attention module exploits global receptive fields that can capture and align long-range correlations between consecutive frames, which can be beneficial for enhancing perceptual quality of videos. The frame to be enhanced is fed into the deep network together with its neighboring frames, and in the first stage features at different depths are extracted. Then extracted features are fed into attention blocks to explore global temporal correlations, followed by a series of upsampling and convolution layers. Finally, the resulting features are processed by the QP-conditional adaptation module which leverages the corresponding QP information. In this way, a single model can be used to enhance adaptively to various QPs without requiring multiple models specific for every QP value, while having similar performance. Experimental results demonstrate the superior performance of the proposed PeQuENet compared with the state-of-the-art compressed video quality enhancement algorithms.
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Notes MACO; no proj Approved no
Call Number Admin @ si @ ZHM2022b Serial 3819
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Author (down) S. Gonzalez; A. Martinez
Title Fundamentos de la Vision aplicada a la Robotica Autonoma. Type Miscellaneous
Year 1997 Publication Abbreviated Journal
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Call Number Admin @ si @ GoM1997 Serial 204
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Author (down) S. Garcia; Dani Rowe; Jordi Gonzalez; Juan J. Villanueva
Title Articulated Object Modelling Using Neural Gas Networks Type Miscellaneous
Year 2005 Publication 5th IASTED International Conference on Visualization, Imaging and Image Processing (VIIP’2005) Abbreviated Journal
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Address Benidorm (Spain)
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Notes Approved no
Call Number ISE @ ise @ GRG2005 Serial 606
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Author (down) Ruben Tito; Khanh Nguyen; Marlon Tobaben; Raouf Kerkouche; Mohamed Ali Souibgui; Kangsoo Jung; Lei Kang; Ernest Valveny; Antti Honkela; Mario Fritz; Dimosthenis Karatzas
Title Privacy-Aware Document Visual Question Answering Type Miscellaneous
Year 2023 Publication Arxiv Abbreviated Journal
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Abstract Document Visual Question Answering (DocVQA) is a fast growing branch of document understanding. Despite the fact that documents contain sensitive or copyrighted information, none of the current DocVQA methods offers strong privacy guarantees.
In this work, we explore privacy in the domain of DocVQA for the first time. We highlight privacy issues in state of the art multi-modal LLM models used for DocVQA, and explore possible solutions.
Specifically, we focus on the invoice processing use case as a realistic, widely used scenario for document understanding, and propose a large scale DocVQA dataset comprising invoice documents and associated questions and answers. We employ a federated learning scheme, that reflects the real-life distribution of documents in different businesses, and we explore the use case where the ID of the invoice issuer is the sensitive information to be protected.
We demonstrate that non-private models tend to memorise, behaviour that can lead to exposing private information. We then evaluate baseline training schemes employing federated learning and differential privacy in this multi-modal scenario, where the sensitive information might be exposed through any of the two input modalities: vision (document image) or language (OCR tokens).
Finally, we design an attack exploiting the memorisation effect of the model, and demonstrate its effectiveness in probing different DocVQA models.
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Notes DAG Approved no
Call Number Admin @ si @ PNT2023 Serial 4012
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Author (down) Ruben Ballester; Xavier Arnal Clemente; Carles Casacuberta; Meysam Madadi; Ciprian Corneanu
Title Towards explaining the generalization gap in neural networks using topological data analysis Type Miscellaneous
Year 2022 Publication Arxiv Abbreviated Journal
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Abstract Understanding how neural networks generalize on unseen data is crucial for designing more robust and reliable models. In this paper, we study the generalization gap of neural networks using methods from topological data analysis. For this purpose, we compute homological persistence diagrams of weighted graphs constructed from neuron activation correlations after a training phase, aiming to capture patterns that are linked to the generalization capacity of the network. We compare the usefulness of different numerical summaries from persistence diagrams and show that a combination of some of them can accurately predict and partially explain the generalization gap without the need of a test set. Evaluation on two computer vision recognition tasks (CIFAR10 and SVHN) shows competitive generalization gap prediction when compared against state-of-the-art methods.
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Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ BAC2022 Serial 3821
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Author (down) Ruben Ballester; Carles Casacuberta; Sergio Escalera
Title Decorrelating neurons using persistence Type Miscellaneous
Year 2023 Publication ARXIV Abbreviated Journal
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Abstract We propose a novel way to improve the generalisation capacity of deep learning models by reducing high correlations between neurons. For this, we present two regularisation terms computed from the weights of a minimum spanning tree of the clique whose vertices are the neurons of a given network (or a sample of those), where weights on edges are correlation dissimilarities. We provide an extensive set of experiments to validate the effectiveness of our terms, showing that they outperform popular ones. Also, we demonstrate that naive minimisation of all correlations between neurons obtains lower accuracies than our regularisation terms, suggesting that redundancies play a significant role in artificial neural networks, as evidenced by some studies in neuroscience for real networks. We include a proof of differentiability of our regularisers, thus developing the first effective topological persistence-based regularisation terms that consider the whole set of neurons and that can be applied to a feedforward architecture in any deep learning task such as classification, data generation, or regression.
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Call Number Admin @ si @ BCE2023 Serial 3977
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Author (down) Robert Benavente; Ramon Baldrich; M.C. Olive; Maria Vanrell
Title Colour Naming Considering the Colour Variability Problem. Type Miscellaneous
Year 2000 Publication Computacion y Sistemas, 4(1):30–43. Abbreviated Journal
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Notes CIC Approved no
Call Number CAT @ cat @ BBO2000 Serial 242
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Author (down) Robert Benavente; Maria Vanrell
Title Fuzzy Colour Naming Based on Sigmoid Membership Functions. Type Miscellaneous
Year 2004 Publication CGIV 2004 Second European Conference on Colour in Graphics, Imaging and Vision, 135:139 Abbreviated Journal
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Address Aachen (Germany)
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Notes CIC Approved no
Call Number CAT @ cat @ BeV2004 Serial 441
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Author (down) Robert Benavente; Maria Vanrell
Title Parametrizacion del Espacio de Categorias de Color Type Miscellaneous
Year 2007 Publication Proceedings del VIII Congreso Nacional del Color Abbreviated Journal
Volume Issue Pages 77–78
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Address Madrid (Spain)
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Area Expedition Conference CNC’07
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Call Number CAT @ cat @ BeV2007 Serial 905
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Author (down) Robert Benavente; M.C. Olive; Maria Vanrell; Ramon Baldrich
Title Colour Perception: A Simple Method for Colour Naming. Type Miscellaneous
Year 1999 Publication Abbreviated Journal
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Address Girona
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Notes CIC Approved no
Call Number CAT @ cat @ BOV1999 Serial 47
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Author (down) Robert Benavente; Francesc Tous; Ramon Baldrich; Maria Vanrell
Title Statical Modelling of a Colour Naming Space. Type Miscellaneous
Year 2002 Publication Proceedings of the 1st. European Conference on Colour in Graphics Imaging and Vision: 406–411. Abbreviated Journal
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Notes CIC Approved no
Call Number CAT @ cat @ BTB2002 Serial 289
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Author (down) Robert Benavente; Ernest Valveny; Jaume Garcia; Agata Lapedriza; Miquel Ferrer; Gemma Sanchez
Title Una experiencia de adaptacion al EEES de las asignaturas de programacion en Ingenieria Informatica Type Miscellaneous
Year 2008 Publication V Congreso Iberoamericano de Docencia Universitaria, pp. 213–216 Abbreviated Journal
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Address Valencia
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Notes OR;DAG;CIC;MV Approved no
Call Number BCNPCL @ bcnpcl @ BVG2008 Serial 1031
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Author (down) Ricardo Toledo; X. Orriols; X. Binefa; Petia Radeva; Jordi Vitria; Juan J. Villanueva
Title Tracking Elongated Structures using Statistical Snakes. Type Miscellaneous
Year 2000 Publication Computer Vision and Pattern Recognition CVPR´00, 1:157–162. Abbreviated Journal
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Notes OR;MILAB;ADAS;MV Approved no
Call Number BCNPCL @ bcnpcl @ TOB2000 Serial 339
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Author (down) Ricardo Toledo; Ramon Baldrich; Ernest Valveny; Petia Radeva
Title Enhancing snakes for vessel detection in angiography images. Type Miscellaneous
Year 2002 Publication Proceedings of the Second IASTED International Conference Visualization, Imaging and Image Proceesing VIIP 2002: 139–144. Abbreviated Journal
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Notes MILAB;DAG;CIC;ADAS Approved no
Call Number BCNPCL @ bcnpcl @ TBV2002 Serial 300
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Author (down) Razieh Rastgoo; Kourosh Kiani; Sergio Escalera; Vassilis Athitsos; Mohammad Sabokrou
Title All You Need In Sign Language Production Type Miscellaneous
Year 2022 Publication Arxiv Abbreviated Journal
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Keywords Sign Language Production; Sign Language Recog- nition; Sign Language Translation; Deep Learning; Survey; Deaf
Abstract Sign Language is the dominant form of communication language used in the deaf and hearing-impaired community. To make an easy and mutual communication between the hearing-impaired and the hearing communities, building a robust system capable of translating the spoken language into sign language and vice versa is fundamental.
To this end, sign language recognition and production are two necessary parts for making such a two-way system. Signlanguage recognition and production need to cope with some critical challenges. In this survey, we review recent advances in
Sign Language Production (SLP) and related areas using deep learning. To have more realistic perspectives to sign language, we present an introduction to the Deaf culture, Deaf centers, psychological perspective of sign language, the main differences between spoken language and sign language. Furthermore, we present the fundamental components of a bi-directional sign language translation system, discussing the main challenges in this area. Also, the backbone architectures and methods in SLP are briefly introduced and the proposed taxonomy on SLP is presented. Finally, a general framework for SLP and performance evaluation, and also a discussion on the recent developments, advantages, and limitations in SLP, commenting on possible lines for future research are presented.
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Notes HuPBA; no menciona Approved no
Call Number Admin @ si @ RKE2022c Serial 3698
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