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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 | |
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Address | Virtual; October 2020 | ||||
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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 | |
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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 | ||||
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Area | Expedition ![]() |
Conference | ECCV | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ BME2020 | Serial | 3519 | ||
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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 | |
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Address | Virtual; August 2020 | ||||
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Language | Summary Language | Original Title | |||
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Area | Expedition ![]() |
Conference | ECCVW | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ AAF2020 | Serial | 3520 | ||
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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 | |
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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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Area | Expedition ![]() |
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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 |
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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. | ||||
Address | May 2022 | ||||
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Area | Expedition ![]() |
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Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ WLW2022 | Serial | 3522 | ||
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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 |
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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. | ||||
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Area | Expedition ![]() |
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ LLW2020b | Serial | 3523 | ||
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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 | |
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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. | ||||
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Area | Expedition ![]() |
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Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ RKE2020 | Serial | 3524 | ||
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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 | |
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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. | ||||
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Area | Expedition ![]() |
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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ AgR2020 | Serial | 3525 | ||
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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 | |
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Area | Expedition ![]() |
Conference | DELTA | ||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ Rad2020 | Serial | 3527 | ||
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Author | Martin Menchon; Estefania Talavera; Jose M. Massa; Petia Radeva | ||||
Title | Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams | Type | Conference Article | ||
Year | 2020 | Publication | ECCV Workshops | Abbreviated Journal | |
Volume | 12538 | Issue | Pages | 469-484 | |
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Abstract | The automatic discovery of behaviour is of high importance when aiming to assess and improve the quality of life of people. Egocentric images offer a rich and objective description of the daily life of the camera wearer. This work proposes a new method to identify a person’s patterns of behaviour from collected egocentric photo-streams. Our model characterizes time-frames based on the context (place, activities and environment objects) that define the images composition. Based on the similarity among the time-frames that describe the collected days for a user, we propose a new unsupervised greedy method to discover the behavioural pattern set based on a novel semantic clustering approach. Moreover, we present a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100k images extracted from 7 users. Results show that behavioural patterns can be discovered to characterize the routine of individuals and consequently their lifestyle. | ||||
Address | Virtual; August 2020 | ||||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Area | Expedition ![]() |
Conference | ECCVW | ||
Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ MTM2020 | Serial | 3528 | ||
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Author | Mariona Caros; Maite Garolera; Petia Radeva; Xavier Giro | ||||
Title | Automatic Reminiscence Therapy for Dementia | Type | Conference Article | ||
Year | 2020 | Publication | 10th ACM International Conference on Multimedia Retrieval | Abbreviated Journal | |
Volume | Issue | Pages | 383-387 | ||
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Abstract | With people living longer than ever, the number of cases with dementia such as Alzheimer's disease increases steadily. It affects more than 46 million people worldwide, and it is estimated that in 2050 more than 100 million will be affected. While there are not effective treatments for these terminal diseases, therapies such as reminiscence, that stimulate memories from the past are recommended. Currently, reminiscence therapy takes place in care homes and is guided by a therapist or a carer. In this work, we present an AI-based solution to automatize the reminiscence therapy, which consists in a dialogue system that uses photos as input to generate questions. We run a usability case study with patients diagnosed of mild cognitive impairment that shows they found the system very entertaining and challenging. Overall, this paper presents how reminiscence therapy can be automatized by using machine learning, and deployed to smartphones and laptops, making the therapy more accessible to every person affected by dementia. | ||||
Address | Virtual; October 2020 | ||||
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Area | Expedition ![]() |
Conference | ICRM | ||
Notes | Approved | no | |||
Call Number | Admin @ si @ CGR2020 | Serial | 3529 | ||
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Author | Giuseppe Pezzano; Vicent Ribas Ripoll; Petia Radeva | ||||
Title | CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation | Type | Journal Article | ||
Year | 2021 | Publication | Computer Methods and Programs in Biomedicine | Abbreviated Journal | CMPB |
Volume | 198 | Issue | Pages | 105792 | |
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Abstract | Background and objective:An accurate segmentation of lung nodules in computed tomography images is a crucial step for the physical characterization of the tumour. Being often completely manually accomplished, nodule segmentation turns to be a tedious and time-consuming procedure and this represents a high obstacle in clinical practice. In this paper, we propose a novel Convolutional Neural Network for nodule segmentation that combines a light and efficient architecture with innovative loss function and segmentation strategy. Methods:In contrast to most of the standard end-to-end architectures for nodule segmentation, our network learns the context of the nodules by producing two masks representing all the background and secondary-important elements in the Computed Tomography scan. The nodule is detected by subtracting the context from the original scan image. Additionally, we introduce an asymmetric loss function that automatically compensates for potential errors in the nodule annotations. We trained and tested our Neural Network on the public LIDC-IDRI database, compared it with the state of the art and run a pseudo-Turing test between four radiologists and the network. Results:The results proved that the behaviour of the algorithm is very near to the human performance and its segmentation masks are almost indistinguishable from the ones made by the radiologists. Our method clearly outperforms the state of the art on CT nodule segmentation in terms of F1 score and IoU of and respectively. Conclusions: The main structure of the network ensures all the properties of the UNet architecture, while the Multi Convolutional Layers give a more accurate pattern recognition. The newly adopted solutions also increase the details on the border of the nodule, even under the noisiest conditions. This method can be applied now for single CT slice nodule segmentation and it represents a starting point for the future development of a fully automatic 3D segmentation software. | ||||
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Area | Expedition ![]() |
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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ PRR2021 | Serial | 3530 | ||
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Author | Esmitt Ramirez; Carles Sanchez; Debora Gil | ||||
Title | Localizing Pulmonary Lesions Using Fuzzy Deep Learning | Type | Conference Article | ||
Year | 2019 | Publication | 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing | Abbreviated Journal | |
Volume | Issue | Pages | 290-294 | ||
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Abstract | The usage of medical images is part of the clinical daily in several healthcare centers around the world. Particularly, Computer Tomography (CT) images are an important key in the early detection of suspicious lung lesions. The CT image exploration allows the detection of lung lesions before any invasive procedure (e.g. bronchoscopy, biopsy). The effective localization of lesions is performed using different image processing and computer vision techniques. Lately, the usage of deep learning models into medical imaging from detection to prediction shown that is a powerful tool for Computer-aided software. In this paper, we present an approach to localize pulmonary lung lesion using fuzzy deep learning. Our approach uses a simple convolutional neural network based using the LIDC-IDRI dataset. Each image is divided into patches associated a probability vector (fuzzy) according their belonging to anatomical structures on a CT. We showcase our approach as part of a full CAD system to exploration, planning, guiding and detection of pulmonary lesions. | ||||
Address | Timisoara; Rumania; September 2019 | ||||
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Area | Expedition ![]() |
Conference | SYNASC | ||
Notes | IAM; 600.145; 600.140; 601.337; 601.323 | Approved | no | ||
Call Number | Admin @ si @ RSG2019 | Serial | 3531 | ||
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Author | Cristina Palmero; Javier Selva; Sorina Smeureanu; Julio C. S. Jacques Junior; Albert Clapes; Alexa Mosegui; Zejian Zhang; David Gallardo; Georgina Guilera; David Leiva; Sergio Escalera | ||||
Title | Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset | Type | Conference Article | ||
Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 1-12 | ||
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Abstract | This paper introduces UDIVA, a new non-acted dataset of face-to-face dyadic interactions, where interlocutors perform competitive and collaborative tasks with different behavior elicitation and cognitive workload. The dataset consists of 90.5 hours of dyadic interactions among 147 participants distributed in 188 sessions, recorded using multiple audiovisual and physiological sensors. Currently, it includes sociodemographic, self- and peer-reported personality, internal state, and relationship profiling from participants. As an initial analysis on UDIVA, we propose a
transformer-based method for self-reported personality inference in dyadic scenarios, which uses audiovisual data and different sources of context from both interlocutors to regress a target person’s personality traits. Preliminary results from an incremental study show consistent improvements when using all available context information. |
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Address | Virtual; January 2021 | ||||
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Area | Expedition ![]() |
Conference | WACV | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ PSS2021 | Serial | 3532 | ||
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Author | Julio C. S. Jacques Junior; Agata Lapedriza; Cristina Palmero; Xavier Baro; Sergio Escalera | ||||
Title | Person Perception Biases Exposed: Revisiting the First Impressions Dataset | Type | Conference Article | ||
Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 13-21 | ||
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Abstract | This work revisits the ChaLearn First Impressions database, annotated for personality perception using pairwise comparisons via crowdsourcing. We analyse for the first time the original pairwise annotations, and reveal existing person perception biases associated to perceived attributes like gender, ethnicity, age and face attractiveness.
We show how person perception bias can influence data labelling of a subjective task, which has received little attention from the computer vision and machine learning communities by now. We further show that the mechanism used to convert pairwise annotations to continuous values may magnify the biases if no special treatment is considered. The findings of this study are relevant for the computer vision community that is still creating new datasets on subjective tasks, and using them for practical applications, ignoring these perceptual biases. |
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Address | Virtual; January 2021 | ||||
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Area | Expedition ![]() |
Conference | WACV | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ JLP2021 | Serial | 3533 | ||
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