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Author M. Li; Xialei Liu; Joost Van de Weijer; Bogdan Raducanu
Title Learning to Rank for Active Learning: A Listwise Approach Type Conference Article
Year 2020 Publication 25th International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages 5587-5594
Keywords
Abstract 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.
Address Virtual; January 2021
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICPR
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ LLW2020a Serial 3511
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Author Gemma Rotger
Title Lifelike Humans: Detailed Reconstruction of Expressive Human Faces Type Book Whole
Year 2021 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract 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.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Felipe Lumbreras;Antonio Agudo
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-122714-3-0 Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ Rot2021 Serial 3513
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Author Ciprian Corneanu; Meysam Madadi; Sergio Escalera; Aleix Martinez
Title Explainable Early Stopping for Action Unit Recognition Type Conference Article
Year 2020 Publication Faces and Gestures in E-health and welfare workshop Abbreviated Journal
Volume Issue Pages 693-699
Keywords
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.
Address Virtual; November 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference FGW
Notes HUPBA; Approved no
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
Title Seniors’ ability to decode differently aged facial emotional expressions Type Conference Article
Year 2020 Publication Faces and Gestures in E-health and welfare workshop Abbreviated Journal
Volume Issue Pages 716-722
Keywords
Abstract
Address Virtual; November 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference FGW
Notes HUPBA Approved no
Call Number Admin @ si @ EAM2020 Serial 3515
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Author Anna Esposito; Italia Cirillo; Antonietta Esposito; Leopoldina Fortunati; Gian Luca Foresti; Sergio Escalera; Nikolaos Bourbakis
Title Impairments in decoding facial and vocal emotional expressions in high functioning autistic adults and adolescents Type Conference Article
Year 2020 Publication Faces and Gestures in E-health and welfare workshop Abbreviated Journal
Volume Issue Pages 667-674
Keywords
Abstract
Address Virtual; November 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference FGW
Notes HUPBA Approved no
Call Number Admin @ si @ ECE2020 Serial 3516
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Author Josep Famadas; Meysam Madadi; Cristina Palmero; Sergio Escalera
Title Generative Video Face Reenactment by AUs and Gaze Regularization Type Conference Article
Year 2020 Publication 15th IEEE International Conference on Automatic Face and Gesture Recognition Abbreviated Journal
Volume Issue Pages 444-451
Keywords
Abstract 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.
Address Virtual; November 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference FG
Notes HUPBA Approved no
Call Number Admin @ si @ FMP2020 Serial 3517
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Author Carlos Martin-Isla; Maryam Asadi-Aghbolaghi; Polyxeni Gkontra; Victor M. Campello; Sergio Escalera; Karim Lekadir
Title Stacked BCDU-net with semantic CMR synthesis: application to Myocardial Pathology Segmentation challenge Type Conference Article
Year 2020 Publication MYOPS challenge and workshop Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Virtual; October 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference MICCAIW
Notes HUPBA Approved no
Call Number Admin @ si @ MAG2020 Serial 3518
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Author Hugo Bertiche; Meysam Madadi; Sergio Escalera
Title CLOTH3D: Clothed 3D Humans Type Conference Article
Year 2020 Publication 16th European Conference on Computer Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract 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.
Address Virtual; August 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ECCV
Notes HUPBA Approved no
Call Number Admin @ si @ BME2020 Serial 3519
Permanent link to this record
 
 
Author Reza Azad; Maryam Asadi-Aghbolaghi; Mahmood Fathy; Sergio Escalera
Title Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation Type Conference Article
Year 2020 Publication Bioimage computation workshop Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Virtual; August 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ECCVW
Notes HUPBA Approved no
Call Number Admin @ si @ AAF2020 Serial 3520
Permanent link to this record
 
 
Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera
Title Sign Language Recognition: A Deep Survey Type Journal Article
Year 2021 Publication Expert Systems With Applications Abbreviated Journal ESWA
Volume 164 Issue Pages 113794
Keywords
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.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; no proj Approved no
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
Title ChaLearn Looking at People: IsoGD and ConGD Large-scale RGB-D Gesture Recognition Type Journal Article
Year 2022 Publication IEEE Transactions on Cybernetics Abbreviated Journal TCIBERN
Volume 52 Issue 5 Pages 3422-3433
Keywords
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.
Address May 2022
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ WLW2022 Serial 3522
Permanent link to this record
 
 
Author Ajian Liu; Xuan Li; Jun Wan; Yanyan Liang; Sergio Escalera; Hugo Jair Escalante; Meysam Madadi; Yi Jin; Zhuoyuan Wu; Xiaogang Yu; Zichang Tan; Qi Yuan; Ruikun Yang; Benjia Zhou; Guodong Guo; Stan Z. Li
Title Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review Type Journal Article
Year 2020 Publication IET Biometrics Abbreviated Journal BIO
Volume 10 Issue 1 Pages 24-43
Keywords
Abstract Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has %possessed achieved impressive progress recently due the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing. Recently, a multi-ethnic face anti-spoofing dataset, CASIA-SURF CeFA, has been released with the goal of measuring the ethnic bias. It is the largest up to date cross-ethnicity face anti-spoofing dataset covering 3 ethnicities, 3 modalities, 1,607 subjects, 2D plus 3D attack types, and the first dataset including explicit ethnic labels among the recently released datasets for face anti-spoofing. We organized the Chalearn Face Anti-spoofing Attack Detection Challenge which consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have attracted 340 teams in the development stage, and finally 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ LLW2020b Serial 3523
Permanent link to this record
 
 
Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera
Title Hand pose aware multimodal isolated sign language recognition Type Journal Article
Year 2020 Publication Multimedia Tools and Applications Abbreviated Journal MTAP
Volume 80 Issue Pages 127–163
Keywords
Abstract Isolated hand sign language recognition from video is a challenging research area in computer vision. Some of the most important challenges in this area include dealing with hand occlusion, fast hand movement, illumination changes, or background complexity. While most of the state-of-the-art results in the field have been achieved using deep learning-based models, the previous challenges are not completely solved. In this paper, we propose a hand pose aware model for isolated hand sign language recognition using deep learning approaches from two input modalities, RGB and depth videos. Four spatial feature types: pixel-level, flow, deep hand, and hand pose features, fused from both visual modalities, are input to LSTM for temporal sign recognition. While we use Optical Flow (OF) for flow information in RGB video inputs, Scene Flow (SF) is used for depth video inputs. By including hand pose features, we show a consistent performance improvement of the sign language recognition model. To the best of our knowledge, this is the first time that this discriminant spatiotemporal features, benefiting from the hand pose estimation features and multi-modal inputs, are fused for isolated hand sign language recognition. We perform a step-by-step analysis of the impact in terms of recognition performance of the hand pose features, different combinations of the spatial features, and different recurrent models, especially LSTM and GRU. Results on four public datasets confirm that the proposed model outperforms the current state-of-the-art models on Montalbano II, MSR Daily Activity 3D, and CAD-60 datasets with a relative accuracy improvement of 1.64%, 6.5%, and 7.6%. Furthermore, our model obtains a competitive results on isoGD dataset with only 0.22% margin lower than the current state-of-the-art model.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ RKE2020 Serial 3524
Permanent link to this record
 
 
Author Eduardo Aguilar; Petia Radeva
Title Uncertainty-aware integration of local and flat classifiers for food recognition Type Journal Article
Year 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 136 Issue Pages 237-243
Keywords
Abstract Food image recognition has recently attracted the attention of many researchers, due to the challenging problem it poses, the ease collection of food images, and its numerous applications to health and leisure. In real applications, it is necessary to analyze and recognize thousands of different foods. For this purpose, we propose a novel prediction scheme based on a class hierarchy that considers local classifiers, in addition to a flat classifier. In order to make a decision about which approach to use, we define different criteria that take into account both the analysis of the Epistemic Uncertainty estimated from the ‘children’ classifiers and the prediction from the ‘parent’ classifier. We evaluate our proposal using three Uncertainty estimation methods, tested on two public food datasets. The results show that the proposed method reduces parent-child error propagation in hierarchical schemes and improves classification results compared to the single flat classifier, meanwhile maintains good performance regardless the Uncertainty estimation method chosen.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB; no proj Approved no
Call Number Admin @ si @ AgR2020 Serial 3525
Permanent link to this record
 
 
Author Petia Radeva
Title Uncertainty Modeling within an End-to-end Framework for Food Image Analysis Type Conference Article
Year 2020 Publication 1st DELTA Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference DELTA
Notes MILAB Approved no
Call Number Admin @ si @ Rad2020 Serial 3527
Permanent link to this record