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Author |
David Berga; Marc Masana; Joost Van de Weijer |
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Title |
Disentanglement of Color and Shape Representations for Continual Learning |
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Conference Article |
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Year |
2020 |
Publication |
ICML Workshop on Continual Learning |
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We hypothesize that disentangled feature representations suffer less from catastrophic forgetting. As a case study we perform explicit disentanglement of color and shape, by adjusting the network architecture. We tested classification accuracy and forgetting in a task-incremental setting with Oxford-102 Flowers dataset. We combine our method with Elastic Weight Consolidation, Learning without Forgetting, Synaptic Intelligence and Memory Aware Synapses, and show that feature disentanglement positively impacts continual learning performance. |
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Virtual; July 2020 |
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ICMLW |
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LAMP; 600.120 |
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no |
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Call Number |
Admin @ si @ BMW2020 |
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3506 |
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Author |
Asma Bensalah; Jialuo Chen; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados; Miguel A. Ferrer |
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Title |
Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches. |
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Conference Article |
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Year |
2020 |
Publication |
International Workshop on Artificial Intelligence for Healthcare Applications |
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12661 |
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476-489 |
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Assessing the physical condition in rehabilitation scenarios is a challenging problem, since it involves Human Activity Recognition (HAR) and kinematic analysis methods. In addition, the difficulties increase in unconstrained rehabilitation scenarios, which are much closer to the real use cases. In particular, our aim is to design an upper-limb assessment pipeline for stroke patients using smartwatches. We focus on the HAR task, as it is the first part of the assessing pipeline. Our main target is to automatically detect and recognize four key movements inspired by the Fugl-Meyer assessment scale, which are performed in both constrained and unconstrained scenarios. In addition to the application protocol and dataset, we propose two detection and classification baseline methods. We believe that the proposed framework, dataset and baseline results will serve to foster this research field. |
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Virtual; January 2021 |
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ICPRW |
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DAG; 600.121; 600.140; |
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Admin @ si @ BCF2020 |
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3508 |
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Author |
Manuel Carbonell; Pau Riba; Mauricio Villegas; Alicia Fornes; Josep Llados |
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Title |
Named Entity Recognition and Relation Extraction with Graph Neural Networks in Semi Structured Documents |
Type |
Conference Article |
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Year |
2020 |
Publication |
25th International Conference on Pattern Recognition |
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The use of administrative documents to communicate and leave record of business information requires of methods
able to automatically extract and understand the content from
such documents in a robust and efficient way. In addition,
the semi-structured nature of these reports is specially suited
for the use of graph-based representations which are flexible
enough to adapt to the deformations from the different document
templates. Moreover, Graph Neural Networks provide the proper
methodology to learn relations among the data elements in
these documents. In this work we study the use of Graph
Neural Network architectures to tackle the problem of entity
recognition and relation extraction in semi-structured documents.
Our approach achieves state of the art results in the three
tasks involved in the process. Additionally, the experimentation
with two datasets of different nature demonstrates the good
generalization ability of our approach. |
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Virtual; January 2021 |
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ICPR |
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DAG; 600.121 |
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Call Number |
Admin @ si @ CRV2020 |
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3509 |
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Author |
M. Li; Xialei Liu; Joost Van de Weijer; Bogdan Raducanu |
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Title |
Learning to Rank for Active Learning: A Listwise Approach |
Type |
Conference Article |
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Year |
2020 |
Publication |
25th International Conference on Pattern Recognition |
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5587-5594 |
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Active learning emerged as an alternative to alleviate the effort to label huge amount of data for data hungry applications (such as image/video indexing and retrieval, autonomous driving, etc.). The goal of active learning is to automatically select a number of unlabeled samples for annotation (according to a budget), based on an acquisition function, which indicates how valuable a sample is for training the model. The learning loss method is a task-agnostic approach which attaches a module to learn to predict the target loss of unlabeled data, and select data with the highest loss for labeling. In this work, we follow this strategy but we define the acquisition function as a learning to rank problem and rethink the structure of the loss prediction module, using a simple but effective listwise approach. Experimental results on four datasets demonstrate that our method outperforms recent state-of-the-art active learning approaches for both image classification and regression tasks. |
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Virtual; January 2021 |
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ICPR |
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LAMP; 600.120 |
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no |
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Call Number |
Admin @ si @ LLW2020a |
Serial |
3511 |
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Author |
Parichehr Behjati Ardakani; Pau Rodriguez; Armin Mehri; Isabelle Hupont; Carles Fernandez; Jordi Gonzalez |
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Title |
OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network |
Type |
Conference Article |
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Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
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2693-2702 |
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Super-resolution (SR) has achieved great success due to the development of deep convolutional neural networks (CNNs). However, as the depth and width of the networks increase, CNN-based SR methods have been faced with the challenge of computational complexity in practice. More- over, most SR methods train a dedicated model for each target resolution, losing generality and increasing memory requirements. To address these limitations we introduce OverNet, a deep but lightweight convolutional network to solve SISR at arbitrary scale factors with a single model. We make the following contributions: first, we introduce a lightweight feature extractor that enforces efficient reuse of information through a novel recursive structure of skip and dense connections. Second, to maximize the performance of the feature extractor, we propose a model agnostic reconstruction module that generates accurate high-resolution images from overscaled feature maps obtained from any SR architecture. Third, we introduce a multi-scale loss function to achieve generalization across scales. Experiments show that our proposal outperforms previous state-of-the-art approaches in standard benchmarks, while maintaining relatively low computation and memory requirements. |
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Virtual; January 2021 |
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WACV |
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ISE; 600.119; 600.098 |
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Call Number |
Admin @ si @ BRM2021 |
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3512 |
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Author |
Gemma Rotger |
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Title |
Lifelike Humans: Detailed Reconstruction of Expressive Human Faces |
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Book Whole |
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Year |
2021 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Developing human-like digital characters is a challenging task since humans are used to recognizing our fellows, and find the computed generated characters inadequately humanized. To fulfill the standards of the videogame and digital film productions it is necessary to model and animate these characters the most closely to human beings. However, it is an arduous and expensive task, since many artists and specialists are required to work on a single character. Therefore, to fulfill these requirements we found an interesting option to study the automatic creation of detailed characters through inexpensive setups. In this work, we develop novel techniques to bring detailed characters by combining different aspects that stand out when developing realistic characters, skin detail, facial hairs, expressions, and microexpressions. We examine each of the mentioned areas with the aim of automatically recover each of the parts without user interaction nor training data. We study the problems for their robustness but also for the simplicity of the setup, preferring single-image with uncontrolled illumination and methods that can be easily computed with the commodity of a standard laptop. A detailed face with wrinkles and skin details is vital to develop a realistic character. In this work, we introduce our method to automatically describe facial wrinkles on the image and transfer to the recovered base face. Then we advance to facial hair recovery by resolving a fitting problem with a novel parametrization model. As of last, we develop a mapping function that allows transfer expressions and microexpressions between different meshes, which provides realistic animations to our detailed mesh. We cover all the mentioned points with the focus on key aspects as (i) how to describe skin wrinkles in a simple and straightforward manner, (ii) how to recover 3D from 2D detections, (iii) how to recover and model facial hair from 2D to 3D, (iv) how to transfer expressions between models holding both skin detail and facial hair, (v) how to perform all the described actions without training data nor user interaction. In this work, we present our proposals to solve these aspects with an efficient and simple setup. We validate our work with several datasets both synthetic and real data, prooving remarkable results even in challenging cases as occlusions as glasses, thick beards, and indeed working with different face topologies like single-eyed cyclops. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Felipe Lumbreras;Antonio Agudo |
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978-84-122714-3-0 |
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ADAS |
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no |
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Call Number |
Admin @ si @ Rot2021 |
Serial |
3513 |
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Author |
Ciprian Corneanu; Meysam Madadi; Sergio Escalera; Aleix Martinez |
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Title |
Explainable Early Stopping for Action Unit Recognition |
Type |
Conference Article |
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Year |
2020 |
Publication |
Faces and Gestures in E-health and welfare workshop |
Abbreviated Journal |
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Pages |
693-699 |
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Abstract |
A common technique to avoid overfitting when training deep neural networks (DNN) is to monitor the performance in a dedicated validation data partition and to stop
training as soon as it saturates. This only focuses on what the model does, while completely ignoring what happens inside it.
In this work, we open the “black-box” of DNN in order to perform early stopping. We propose to use a novel theoretical framework that analyses meso-scale patterns in the topology of the functional graph of a network while it trains. Based on it,
we decide when it transitions from learning towards overfitting in a more explainable way. We exemplify the benefits of this approach on a state-of-the art custom DNN that jointly learns local representations and label structure employing an ensemble of dedicated subnetworks. We show that it is practically equivalent in performance to early stopping with patience, the standard early stopping algorithm in the literature. This proves beneficial for AU recognition performance and provides new insights into how learning of AUs occurs in DNNs. |
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Virtual; November 2020 |
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FGW |
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HUPBA; |
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no |
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Call Number |
Admin @ si @ CME2020 |
Serial |
3514 |
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Author |
Anna Esposito; Terry Amorese; Nelson Maldonato; Alessandro Vinciarelli; Maria Ines Torres; Sergio Escalera; Gennaro Cordasco |
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Title |
Seniors’ ability to decode differently aged facial emotional expressions |
Type |
Conference Article |
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2020 |
Publication |
Faces and Gestures in E-health and welfare workshop |
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716-722 |
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Virtual; November 2020 |
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FGW |
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HUPBA |
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no |
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Admin @ si @ EAM2020 |
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3515 |
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Author |
Anna Esposito; Italia Cirillo; Antonietta Esposito; Leopoldina Fortunati; Gian Luca Foresti; Sergio Escalera; Nikolaos Bourbakis |
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Title |
Impairments in decoding facial and vocal emotional expressions in high functioning autistic adults and adolescents |
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Conference Article |
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2020 |
Publication |
Faces and Gestures in E-health and welfare workshop |
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667-674 |
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Virtual; November 2020 |
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FGW |
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HUPBA |
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no |
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Call Number |
Admin @ si @ ECE2020 |
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3516 |
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Author |
Josep Famadas; Meysam Madadi; Cristina Palmero; Sergio Escalera |
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Title |
Generative Video Face Reenactment by AUs and Gaze Regularization |
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Conference Article |
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Year |
2020 |
Publication |
15th IEEE International Conference on Automatic Face and Gesture Recognition |
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444-451 |
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In this work, we propose an encoder-decoder-like architecture to perform face reenactment in image sequences. Our goal is to transfer the training subject identity to a given test subject. We regularize face reenactment by facial action unit intensity and 3D gaze vector regression. This way, we enforce the network to transfer subtle facial expressions and eye dynamics, providing a more lifelike result. The proposed encoder-decoder receives as input the previous sequence frame stacked to the current frame image of facial landmarks. Thus, the generated frames benefit from appearance and geometry, while keeping temporal coherence for the generated sequence. At test stage, a new target subject with the facial performance of the source subject and the appearance of the training subject is reenacted. Principal component analysis is applied to project the test subject geometry to the closest training subject geometry before reenactment. Evaluation of our proposal shows faster convergence, and more accurate and realistic results in comparison to other architectures without action units and gaze regularization. |
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Virtual; November 2020 |
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HUPBA |
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no |
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Admin @ si @ FMP2020 |
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3517 |
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Author |
Carlos Martin-Isla; Maryam Asadi-Aghbolaghi; Polyxeni Gkontra; Victor M. Campello; Sergio Escalera; Karim Lekadir |
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Title |
Stacked BCDU-net with semantic CMR synthesis: application to Myocardial Pathology Segmentation challenge |
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2020 |
Publication |
MYOPS challenge and workshop |
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Virtual; October 2020 |
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MICCAIW |
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HUPBA |
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no |
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Admin @ si @ MAG2020 |
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3518 |
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Author |
Hugo Bertiche; Meysam Madadi; Sergio Escalera |
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Title |
CLOTH3D: Clothed 3D Humans |
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Conference Article |
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2020 |
Publication |
16th European Conference on Computer Vision |
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This work presents CLOTH3D, the first big scale synthetic dataset of 3D clothed human sequences. CLOTH3D contains a large variability on garment type, topology, shape, size, tightness and fabric. Clothes are simulated on top of thousands of different pose sequences and body shapes, generating realistic cloth dynamics. We provide the dataset with a generative model for cloth generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on graph convolutions (GCVAE) to learn garment latent spaces. This allows for realistic generation of 3D garments on top of SMPL model for any pose and shape. |
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Virtual; August 2020 |
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ECCV |
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HUPBA |
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no |
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Admin @ si @ BME2020 |
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3519 |
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Author |
Reza Azad; Maryam Asadi-Aghbolaghi; Mahmood Fathy; Sergio Escalera |
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Title |
Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation |
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Conference Article |
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2020 |
Publication |
Bioimage computation workshop |
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Virtual; August 2020 |
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ECCVW |
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HUPBA |
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Admin @ si @ AAF2020 |
Serial |
3520 |
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Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
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Title |
Sign Language Recognition: A Deep Survey |
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Journal Article |
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2021 |
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Expert Systems With Applications |
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ESWA |
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164 |
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113794 |
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Abstract |
Sign language, as a different form of the communication language, is important to large groups of people in society. There are different signs in each sign language with variability in hand shape, motion profile, and position of the hand, face, and body parts contributing to each sign. So, visual sign language recognition is a complex research area in computer vision. Many models have been proposed by different researchers with significant improvement by deep learning approaches in recent years. In this survey, we review the vision-based proposed models of sign language recognition using deep learning approaches from the last five years. While the overall trend of the proposed models indicates a significant improvement in recognition accuracy in sign language recognition, there are some challenges yet that need to be solved. We present a taxonomy to categorize the proposed models for isolated and continuous sign language recognition, discussing applications, datasets, hybrid models, complexity, and future lines of research in the field. |
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HUPBA; no proj |
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no |
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Call Number |
Admin @ si @ RKE2021a |
Serial |
3521 |
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Author |
Jun Wan; Chi Lin; Longyin Wen; Yunan Li; Qiguang Miao; Sergio Escalera; Gholamreza Anbarjafari; Isabelle Guyon; Guodong Guo; Stan Z. Li |
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Title |
ChaLearn Looking at People: IsoGD and ConGD Large-scale RGB-D Gesture Recognition |
Type |
Journal Article |
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Year |
2022 |
Publication |
IEEE Transactions on Cybernetics |
Abbreviated Journal |
TCIBERN |
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52 |
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5 |
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3422-3433 |
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Abstract |
The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than 200 teams round the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. This paper describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. We discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition, and provide a detailed analysis of the current state-of-the-art methods for large-scale isolated and continuous gesture recognition based on RGB-D video sequences. In addition to recognition rate and mean jaccard index (MJI) as evaluation metrics used in our previous challenges, we also introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) baseline method, determining the video division points based on the skeleton points extracted by convolutional pose machine (CPM). Experiments demonstrate that the proposed Bi-LSTM outperforms the state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR. |
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May 2022 |
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HUPBA; no menciona |
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no |
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Admin @ si @ WLW2022 |
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3522 |
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| Permanent link to this record |
