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| Author | David Pujol Perich; Albert Clapes; Sergio Escalera | ||||
| Title | SADA: Semantic adversarial unsupervised domain adaptation for Temporal Action Localization | Type | Miscellaneous | ||
| Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Temporal Action Localization (TAL) is a complex task that poses relevant challenges, particularly when attempting to generalize on new -- unseen -- domains in real-world applications. These scenarios, despite realistic, are often neglected in the literature, exposing these solutions to important performance degradation. In this work, we tackle this issue by introducing, for the first time, an approach for Unsupervised Domain Adaptation (UDA) in sparse TAL, which we refer to as Semantic Adversarial unsupervised Domain Adaptation (SADA). Our contributions are threefold: (1) we pioneer the development of a domain adaptation model that operates on realistic sparse action detection benchmarks; (2) we tackle the limitations of global-distribution alignment techniques by introducing a novel adversarial loss that is sensitive to local class distributions, ensuring finer-grained adaptation; and (3) we present a novel set of benchmarks based on EpicKitchens100 and CharadesEgo, that evaluate multiple domain shifts in a comprehensive manner. Our experiments indicate that SADA improves the adaptation across domains when compared to fully supervised state-of-the-art and alternative UDA methods, attaining a performance boost of up to 6.14% mAP. | ||||
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HUPBA | Approved | no | ||
| Call Number | Admin @ si @ PCE2023 | Serial | 4014 | ||
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| Author | 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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HUPBA | Approved | no | ||
| Call Number | Admin @ si @ BCE2023 | Serial | 3977 | ||
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| Author | Anthony Cioppa; Silvio Giancola; Vladimir Somers; Floriane Magera; Xin Zhou; Hassan Mkhallati; Adrien Deliège; Jan Held; Carlos Hinojosa; Amir M. Mansourian; Pierre Miralles; Olivier Barnich; Christophe De Vleeschouwer; Alexandre Alahi; Bernard Ghanem; Marc Van Droogenbroeck; Abdullah Kamal; Adrien Maglo; Albert Clapes; Amr Abdelaziz; Artur Xarles; Astrid Orcesi; Atom Scott; Bin Liu; Byoungkwon Lim; Chen Chen; Fabian Deuser; Feng Yan; Fufu Yu; Gal Shitrit; Guanshuo Wang; Gyusik Choi; Hankyul Kim; Hao Guo; Hasby Fahrudin; Hidenari Koguchi; Håkan Ardo; Ibrahim Salah; Ido Yerushalmy; Iftikar Muhammad; Ikuma Uchida; Ishay Beery; Jaonary Rabarisoa; Jeongae Lee; Jiajun Fu; Jianqin Yin; Jinghang Xu; Jongho Nang; Julien Denize; Junjie Li; Junpei Zhang; Juntae Kim; Kamil Synowiec; Kenji Kobayashi; Kexin Zhang; Konrad Habel; Kota Nakajima; Licheng Jiao; Lin Ma; Lizhi Wang; Luping Wang; Menglong Li; Mengying Zhou; Mohamed Nasr; Mohamed Abdelwahed; Mykola Liashuha; Nikolay Falaleev; Norbert Oswald; Qiong Jia; Quoc-Cuong Pham; Ran Song; Romain Herault; Rui Peng; Ruilong Chen; Ruixuan Liu; Ruslan Baikulov; Ryuto Fukushima; Sergio Escalera; Seungcheon Lee; Shimin Chen; Shouhong Ding; Taiga Someya; Thomas B. Moeslund; Tianjiao Li; Wei Shen; Wei Zhang; Wei Li; Wei Dai; Weixin Luo; Wending Zhao; Wenjie Zhang; Xinquan Yang; Yanbiao Ma; Yeeun Joo; Yingsen Zeng; Yiyang Gan; Yongqiang Zhu; Yujie Zhong; Zheng Ruan; Zhiheng Li; Zhijian Huang; Ziyu Meng | ||||
| Title | SoccerNet 2023 Challenges Results | Type | Miscellaneous | ||
| Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leaderboards are available on this https URL. Baselines and development kits can be found on this https URL. | ||||
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HUPBA | Approved | no | ||
| Call Number | Admin @ si @ CGS2023 | Serial | 3991 | ||
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| Author | Mustafa Hajij; Mathilde Papillon; Florian Frantzen; Jens Agerberg; Ibrahem AlJabea; Ruben Ballester; Claudio Battiloro; Guillermo Bernardez; Tolga Birdal; Aiden Brent; Peter Chin; Sergio Escalera; Simone Fiorellino; Odin Hoff Gardaa; Gurusankar Gopalakrishnan; Devendra Govil; Josef Hoppe; Maneel Reddy Karri; Jude Khouja; Manuel Lecha; Neal Livesay; Jan Meibner; Soham Mukherjee; Alexander Nikitin; Theodore Papamarkou; Jaro Prilepok; Karthikeyan Natesan Ramamurthy; Paul Rosen; Aldo Guzman-Saenz; Alessandro Salatiello; Shreyas N. Samaga; Simone Scardapane; Michael T. Schaub; Luca Scofano; Indro Spinelli; Lev Telyatnikov; Quang Truong; Robin Walters; Maosheng Yang; Olga Zaghen; Ghada Zamzmi; Ali Zia; Nina Miolane | ||||
| Title | TopoX: A Suite of Python Packages for Machine Learning on Topological Domains | Type | Miscellaneous | ||
| Year | 2024 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. TopoX consists of three packages: TopoNetX facilitates constructing and computing on these domains, including working with nodes, edges and higher-order cells; TopoEmbedX provides methods to embed topological domains into vector spaces, akin to popular graph-based embedding algorithms such as node2vec; TopoModelx is built on top of PyTorch and offers a comprehensive toolbox of higher-order message passing functions for neural networks on topological domains. The extensively documented and unit-tested source code of TopoX is available under MIT license at this https URL. | ||||
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HUPBA | Approved | no | ||
| Call Number | Admin @ si @ HPF2024 | Serial | 4021 | ||
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| Author | German Barquero; Sergio Escalera; Cristina Palmero | ||||
| Title | Seamless Human Motion Composition with Blended Positional Encodings | Type | Miscellaneous | ||
| Year | 2024 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Conditional human motion generation is an important topic with many applications in virtual reality, gaming, and robotics. While prior works have focused on generating motion guided by text, music, or scenes, these typically result in isolated motions confined to short durations. Instead, we address the generation of long, continuous sequences guided by a series of varying textual descriptions. In this context, we introduce FlowMDM, the first diffusion-based model that generates seamless Human Motion Compositions (HMC) without any postprocessing or redundant denoising steps. For this, we introduce the Blended Positional Encodings, a technique that leverages both absolute and relative positional encodings in the denoising chain. More specifically, global motion coherence is recovered at the absolute stage, whereas smooth and realistic transitions are built at the relative stage. As a result, we achieve state-of-the-art results in terms of accuracy, realism, and smoothness on the Babel and HumanML3D datasets. FlowMDM excels when trained with only a single description per motion sequence thanks to its Pose-Centric Cross-ATtention, which makes it robust against varying text descriptions at inference time. Finally, to address the limitations of existing HMC metrics, we propose two new metrics: the Peak Jerk and the Area Under the Jerk, to detect abrupt transitions. | ||||
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HUPBA | Approved | no | ||
| Call Number | Admin @ si @ BEP2024 | Serial | 4022 | ||
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| Author | Umut Guclu; Yagmur Gucluturk; Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez; Rob van Lier; Marcel A. J. van Gerven | ||||
| Title | End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks | Type | Miscellaneous | ||
| Year | 2017 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | arXiv:1703.03305
Recent years have seen a sharp increase in the number of related yet distinct advances in semantic segmentation. Here, we tackle this problem by leveraging the respective strengths of these advances. That is, we formulate a conditional random field over a four-connected graph as end-to-end trainable convolutional and recurrent networks, and estimate them via an adversarial process. Importantly, our model learns not only unary potentials but also pairwise potentials, while aggregating multi-scale contexts and controlling higher-order inconsistencies. We evaluate our model on two standard benchmark datasets for semantic face segmentation, achieving state-of-the-art results on both of them. |
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HuPBA; ISE; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ GGM2017 | Serial | 2932 | ||
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| Author | Sergio Escalera; Petia Radeva | ||||
| Title | Fast greyscale road sign model matching and recognition | Type | Miscellaneous | ||
| Year | 2004 | Publication | CCIA, IOS Press | Abbreviated Journal | |
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| Address | Barcelona, Spain | ||||
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HuPBA; MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ EsR2004 | Serial | 469 | ||
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| Author | 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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HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ RKE2022c | Serial | 3698 | ||
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| Author | 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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HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ BAC2022 | Serial | 3821 | ||
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| Author | Arya Farkhondeh; Cristina Palmero; Simone Scardapane; Sergio Escalera | ||||
| Title | Towards Self-Supervised Gaze Estimation | Type | Miscellaneous | ||
| Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Recent joint embedding-based self-supervised methods have surpassed standard supervised approaches on various image recognition tasks such as image classification. These self-supervised methods aim at maximizing agreement between features extracted from two differently transformed views of the same image, which results in learning an invariant representation with respect to appearance and geometric image transformations. However, the effectiveness of these approaches remains unclear in the context of gaze estimation, a structured regression task that requires equivariance under geometric transformations (e.g., rotations, horizontal flip). In this work, we propose SwAT, an equivariant version of the online clustering-based self-supervised approach SwAV, to learn more informative representations for gaze estimation. We demonstrate that SwAT, with ResNet-50 and supported with uncurated unlabeled face images, outperforms state-of-the-art gaze estimation methods and supervised baselines in various experiments. In particular, we achieve up to 57% and 25% improvements in cross-dataset and within-dataset evaluation tasks on existing benchmarks (ETH-XGaze, Gaze360, and MPIIFaceGaze). | ||||
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HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ FPS2022 | Serial | 3822 | ||
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| Author | Razieh Rastgoo; Kourosh Kiani; Sergio Escalera | ||||
| Title | Word separation in continuous sign language using isolated signs and post-processing | Type | Miscellaneous | ||
| Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | 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 | Approved | no | ||
| Call Number | Admin @ si @ RKE2022b | Serial | 3824 | ||
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| Author | Razieh Rastgoo; Kourosh Kiani; Sergio Escalera | ||||
| Title | A Non-Anatomical Graph Structure for isolated hand gesture separation in continuous gesture sequences | Type | Miscellaneous | ||
| Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | 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 | Approved | no | ||
| Call Number | Admin @ si @ RKE2022d | Serial | 3828 | ||
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| Author | Albert Andaluz; Francesc Carreras; Debora Gil; Jaume Garcia | ||||
| Title | Una aplicació amigable pel càlcul de indicadors clínics del ventricle esquerre | Type | Miscellaneous | ||
| Year | 2010 | Publication | Forum Biocat 2010 | Abbreviated Journal | |
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| Address | Lonja de Mar,Barcelona (Spain) | ||||
| Corporate Author | CVC | Thesis | |||
| Publisher | Biocat | Place of Publication | Barcelona | Editor | |
| Language | Catalan | Summary Language | Original Title | ||
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IAM | Approved | no | ||
| Call Number | IAM @ iam @ ACG2010 | Serial | 1483 | ||
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| Author | J.L.Bruguera; R.Casado; M.Martinez; I.Corral; Enric Marti; L.A.Branda | ||||
| Title | El apoyo institucional como elemento favorecedor de la coordinación docente: experiencias en diferentes universidades | Type | Miscellaneous | ||
| Year | 2009 | Publication | Revista Red-U | Abbreviated Journal | |
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IAM | Approved | no | ||
| Call Number | IAM @ iam @ BCM2009 | Serial | 1490 | ||
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| Author | G.Estape; Enric Marti | ||||
| Title | L’ús d’aplicacions de visualització 3D com a eina d’aprenenetatge en activitats formatives dirigides i autònomes: el cas del programa Bluestar | Type | Miscellaneous | ||
| Year | 2008 | Publication | V Jornades d’Innovació Docent UAB | Abbreviated Journal | |
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IAM | Approved | no | ||
| Call Number | IAM @ iam @ ESM2008 | Serial | 1495 | ||
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