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| Author | Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes | ||||
| Title | Learning Graph Edit Distance by Graph NeuralNetworks | Type | Miscellaneous | ||
| Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words~\ie~keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset. | ||||
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| Notes | DAG; 600.121; 600.140; 601.302 | Approved | no | ||
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Admin @ si @ RFL2020 | Serial | 3555 | ||
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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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| Notes | HUPBA; no menciona | Approved | no | ||
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Admin @ si @ RKE2022b | Serial | 3824 | ||
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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 | |
| Volume | Issue | Pages | |||
| 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 | ||
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Admin @ si @ RKE2022c | Serial | 3698 | ||
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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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| Notes | HuPBA; no menciona | Approved | no | ||
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Admin @ si @ RKE2022d | Serial | 3828 | ||
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| Author | Misael Rosales | ||||
| Title | Empirical Simulation Moldel of Intravascular Ultrasound | Type | Miscellaneous | ||
| Year | 2002 | Publication | Director: P. Radeva, Master Thesis. | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | Approved | no | |||
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Admin @ si @ Ros2002 | Serial | 323 | ||
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| Author | David Rotger | ||||
| Title | Multimodal Registration of Intravascular Ultrasound Images and Angiography | Type | Miscellaneous | ||
| Year | 2002 | Publication | Director: P. Radeva, Master Thesis. | Abbreviated Journal | |
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| Notes | Approved | no | |||
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Admin @ si @ Rot2002 | Serial | 324 | ||
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| Author | Dani Rowe | ||||
| Title | Towards Robust Multiple-Target Tracking in Unconstrained Human-Populated Environments | Type | Miscellaneous | ||
| Year | 2008 | Publication | CVC–UAB | Abbreviated Journal | |
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| ISSN | ISBN | 978–84–935251–5–6 | Medium | ||
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| Notes | Approved | no | |||
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Admin @ si @ Row2008 | Serial | 1103 | ||
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| Author | Adriana Romero; Petia Radeva; Carlo Gatta | ||||
| Title | No more meta-parameter tuning in unsupervised sparse feature learning | Type | Miscellaneous | ||
| Year | 2014 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | CoRR abs/1402.5766
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well. |
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| Notes | MILAB; LAMP; 600.079 | Approved | no | ||
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Admin @ si @ RRG2014 | Serial | 2471 | ||
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| Author | J.M. Sanchez; X. Binefa | ||||
| Title | Automatic digital TV commercial recognition. | Type | Miscellaneous | ||
| Year | 1999 | Publication | Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes, 1: 313–320 | Abbreviated Journal | |
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| Address | Bilbao. | ||||
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| Notes | Approved | no | |||
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Admin @ si @ SaV1999 | Serial | 181 | ||
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| Author | J.M. Sanchez; X. Binefa | ||||
| Title | Semantics from motion in news videos. | Type | Miscellaneous | ||
| Year | 2001 | Publication | Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis, 1:79–84. | Abbreviated Journal | |
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| Notes | Approved | no | |||
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Admin @ si @ SBi2001 | Serial | 210 | ||
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| Author | J.M. Sanchez; X. Binefa; J.R. Kender | ||||
| Title | Coupled Markox Chains for Video Contents Characterization. | Type | Miscellaneous | ||
| Year | 2002 | Publication | Proceeding of the International Conference on Pattern Recognition ICPR 2002 | Abbreviated Journal | |
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| Notes | Approved | no | |||
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Admin @ si @ SBK2002a | Serial | 298 | ||
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| Author | J.M. Sanchez; X. Binefa; J.R. Kender | ||||
| Title | Multiple Feature Temporal Models for Object Detection in Video. | Type | Miscellaneous | ||
| Year | 2002 | Publication | Proceeding of the International Conference on Multimedia and Expo ICME 2002 | Abbreviated Journal | |
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| Address | Lausanne | ||||
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| Notes | Approved | no | |||
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Admin @ si @ SBK2002b | Serial | 299 | ||
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| Author | Bonifaz Stuhr; Jurgen Brauer; Bernhard Schick; Jordi Gonzalez | ||||
| Title | Masked Discriminators for Content-Consistent Unpaired Image-to-Image Translation | Type | Miscellaneous | ||
| Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | A common goal of unpaired image-to-image translation is to preserve content consistency between source images and translated images while mimicking the style of the target domain. Due to biases between the datasets of both domains, many methods suffer from inconsistencies caused by the translation process. Most approaches introduced to mitigate these inconsistencies do not constrain the discriminator, leading to an even more ill-posed training setup. Moreover, none of these approaches is designed for larger crop sizes. In this work, we show that masking the inputs of a global discriminator for both domains with a content-based mask is sufficient to reduce content inconsistencies significantly. However, this strategy leads to artifacts that can be traced back to the masking process. To reduce these artifacts, we introduce a local discriminator that operates on pairs of small crops selected with a similarity sampling strategy. Furthermore, we apply this sampling strategy to sample global input crops from the source and target dataset. In addition, we propose feature-attentive denormalization to selectively incorporate content-based statistics into the generator stream. In our experiments, we show that our method achieves state-of-the-art performance in photorealistic sim-to-real translation and weather translation and also performs well in day-to-night translation. Additionally, we propose the cKVD metric, which builds on the sKVD metric and enables the examination of translation quality at the class or category level. | ||||
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| Notes | ISE | Approved | no | ||
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Admin @ si @ SBS2023 | Serial | 3863 | ||
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| Author | Damian Sojka; Yuyang Liu; Dipam Goswami; Sebastian Cygert; Bartłomiej Twardowski; Joost van de Weijer | ||||
| Title | Technical Report for ICCV 2023 Visual Continual Learning Challenge: Continuous Test-time Adaptation for Semantic Segmentation | Type | Miscellaneous | ||
| Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | The goal of the challenge is to develop a test-time adaptation (TTA) method, which could adapt the model to gradually changing domains in video sequences for semantic segmentation task. It is based on a synthetic driving video dataset – SHIFT. The source model is trained on images taken during daytime in clear weather. Domain changes at test-time are mainly caused by varying weather conditions and times of day. The TTA methods are evaluated in each image sequence (video) separately, meaning the model is reset to the source model state before the next sequence. Images come one by one and a prediction has to be made at the arrival of each frame. Each sequence is composed of 401 images and starts with the source domain, then gradually drifts to a different one (changing weather or time of day) until the middle of the sequence. In the second half of the sequence, the domain gradually shifts back to the source one. Ground truth data is available only for the validation split of the SHIFT dataset, in which there are only six sequences that start and end with the source domain. We conduct an analysis specifically on those sequences. Ground truth data for test split, on which the developed TTA methods are evaluated for leader board ranking, are not publicly available.
The proposed solution secured a 3rd place in a challenge and received an innovation award. Contrary to the solutions that scored better, we did not use any external pretrained models or specialized data augmentations, to keep the solutions as general as possible. We have focused on analyzing the distributional shift and developing a method that could adapt to changing data dynamics and generalize across different scenarios. |
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| Notes | LAMP | Approved | no | ||
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Admin @ si @ SLG2023 | Serial | 3993 | ||
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| Author | Carles Sanchez; Oriol Ramos Terrades; Patricia Marquez; Enric Marti; Jaume Rocarias; Debora Gil | ||||
| Title | Evaluación automática de prácticas en Moodle para el aprendizaje autónomo en Ingenierías | Type | Miscellaneous | ||
| Year | 2014 | Publication | 8th International Congress on University Teaching and Innovation | Abbreviated Journal | |
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| Address | Tarragona; juliol 2014 | ||||
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| Area | Expedition | Conference | CIDUI | ||
| Notes | IAM; 600.075;DAG | Approved | no | ||
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Admin @ si @ SRM2014 | Serial | 2458 | ||
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