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Ruben Tito; Khanh Nguyen; Marlon Tobaben; Raouf Kerkouche; Mohamed Ali Souibgui; Kangsoo Jung; Lei Kang; Ernest Valveny; Antti Honkela; Mario Fritz; Dimosthenis Karatzas |
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Title |
Privacy-Aware Document Visual Question Answering |
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Miscellaneous |
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2023 |
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Arxiv |
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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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DAG |
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Admin @ si @ PNT2023 |
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4012 |
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Ruben Ballester; Xavier Arnal Clemente; Carles Casacuberta; Meysam Madadi; Ciprian Corneanu |
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Towards explaining the generalization gap in neural networks using topological data analysis |
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2022 |
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Arxiv |
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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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HUPBA; no menciona |
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Admin @ si @ BAC2022 |
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3821 |
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Ruben Ballester; Carles Casacuberta; Sergio Escalera |
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Title |
Decorrelating neurons using persistence |
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2023 |
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ARXIV |
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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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HUPBA |
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Admin @ si @ BCE2023 |
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3977 |
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Robert Benavente; Ramon Baldrich; M.C. Olive; Maria Vanrell |
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Title |
Colour Naming Considering the Colour Variability Problem. |
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2000 |
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Computacion y Sistemas, 4(1):30–43. |
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CIC |
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CAT @ cat @ BBO2000 |
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242 |
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Robert Benavente; Maria Vanrell |
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Title |
Fuzzy Colour Naming Based on Sigmoid Membership Functions. |
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2004 |
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CGIV 2004 Second European Conference on Colour in Graphics, Imaging and Vision, 135:139 |
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Aachen (Germany) |
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CIC |
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CAT @ cat @ BeV2004 |
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441 |
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Author |
Robert Benavente; Maria Vanrell |
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Title |
Parametrizacion del Espacio de Categorias de Color |
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2007 |
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Proceedings del VIII Congreso Nacional del Color |
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77–78 |
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Madrid (Spain) |
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CNC’07 |
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CAT;CIC |
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CAT @ cat @ BeV2007 |
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905 |
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Robert Benavente; Francesc Tous; Ramon Baldrich; Maria Vanrell |
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Title |
Statical Modelling of a Colour Naming Space. |
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2002 |
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Proceedings of the 1st. European Conference on Colour in Graphics Imaging and Vision: 406–411. |
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CAT @ cat @ BTB2002 |
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289 |
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Robert Benavente; Ernest Valveny; Jaume Garcia; Agata Lapedriza; Miquel Ferrer; Gemma Sanchez |
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Una experiencia de adaptacion al EEES de las asignaturas de programacion en Ingenieria Informatica |
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2008 |
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V Congreso Iberoamericano de Docencia Universitaria, pp. 213–216 |
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Valencia |
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OR;DAG;CIC;MV |
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BCNPCL @ bcnpcl @ BVG2008 |
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1031 |
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Ricardo Toledo; X. Orriols; X. Binefa; Petia Radeva; Jordi Vitria; Juan J. Villanueva |
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Tracking Elongated Structures using Statistical Snakes. |
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2000 |
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Computer Vision and Pattern Recognition CVPR´00, 1:157–162. |
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OR;MILAB;ADAS;MV |
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BCNPCL @ bcnpcl @ TOB2000 |
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339 |
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Ricardo Toledo; Ramon Baldrich; Ernest Valveny; Petia Radeva |
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Enhancing snakes for vessel detection in angiography images. |
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2002 |
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Proceedings of the Second IASTED International Conference Visualization, Imaging and Image Proceesing VIIP 2002: 139–144. |
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MILAB;DAG;CIC;ADAS |
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BCNPCL @ bcnpcl @ TBV2002 |
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300 |
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Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera; Vassilis Athitsos; Mohammad Sabokrou |
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Title |
All You Need In Sign Language Production |
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2022 |
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Arxiv |
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Sign Language Production; Sign Language Recog- nition; Sign Language Translation; Deep Learning; Survey; Deaf |
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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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HuPBA; no menciona |
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no |
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Admin @ si @ RKE2022c |
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3698 |
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Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
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Word separation in continuous sign language using isolated signs and post-processing |
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2022 |
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Arxiv |
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Continuous Sign Language Recognition (CSLR) is a long challenging task in Computer Vision due to the difficulties in detecting the explicit boundaries between the words in a sign sentence. To deal with this challenge, we propose a two-stage model. In the first stage, the predictor model, which includes a combination of CNN, SVD, and LSTM, is trained with the isolated signs. In the second stage, we apply a post-processing algorithm to the Softmax outputs obtained from the first part of the model in order to separate the isolated signs in the continuous signs. Due to the lack of a large dataset, including both the sign sequences and the corresponding isolated signs, two public datasets in Isolated Sign Language Recognition (ISLR), RKS-PERSIANSIGN and ASLVID, are used for evaluation. Results of the continuous sign videos confirm the efficiency of the proposed model to deal with isolated sign boundaries detection. |
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HUPBA; no menciona |
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Admin @ si @ RKE2022b |
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3824 |
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Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
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A Non-Anatomical Graph Structure for isolated hand gesture separation in continuous gesture sequences |
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2022 |
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Arxiv |
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Continuous Hand Gesture Recognition (CHGR) has been extensively studied by researchers in the last few decades. Recently, one model has been presented to deal with the challenge of the boundary detection of isolated gestures in a continuous gesture video [17]. To enhance the model performance and also replace the handcrafted feature extractor in the presented model in [17], we propose a GCN model and combine it with the stacked Bi-LSTM and Attention modules to push the temporal information in the video stream. Considering the breakthroughs of GCN models for skeleton modality, we propose a two-layer GCN model to empower the 3D hand skeleton features. Finally, the class probabilities of each isolated gesture are fed to the post-processing module, borrowed from [17]. Furthermore, we replace the anatomical graph structure with some non-anatomical graph structures. Due to the lack of a large dataset, including both the continuous gesture sequences and the corresponding isolated gestures, three public datasets in Dynamic Hand Gesture Recognition (DHGR), RKS-PERSIANSIGN, and ASLVID, are used for evaluation. Experimental results show the superiority of the proposed model in dealing with isolated gesture boundaries detection in continuous gesture sequences |
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HuPBA; no menciona |
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Admin @ si @ RKE2022d |
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3828 |
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Author |
Ramon Baldrich; Ricardo Toledo; Ernest Valveny; Maria Vanrell |
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Title |
Perceptual Colour Image Segmentation. |
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2002 |
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Proceeding of the Second IASTED International Conference Visualization, Imaging and Image Proceesing VIIP 2002: 145–150. |
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DAG;CIC;ADAS |
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CAT @ cat @ BTV2002 |
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290 |
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Ramon Baldrich; Maria Vanrell; Robert Benavente; Anna Salvatella |
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Color Enhancement based on perceptual sharpening |
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2003 |
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Proceedings of the IEEE International Conference on Image Processing |
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Barcelona |
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Area |
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Expedition |
|
Conference |
|
|
|
Notes |
CIC |
Approved |
no |
|
|
Call Number |
CAT @ cat @ BVB2003 |
Serial |
370 |
|
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