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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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no |
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Call Number |
Admin @ si @ CRV2020 |
Serial |
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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Notes |
LAMP; 600.120 |
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no |
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Call Number |
Admin @ si @ LLW2020a |
Serial |
3511 |
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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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Year |
2020 |
Publication |
Faces and Gestures in E-health and welfare workshop |
Abbreviated Journal |
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Pages |
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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Call Number |
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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Year |
2020 |
Publication |
Faces and Gestures in E-health and welfare workshop |
Abbreviated Journal |
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Pages |
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 |
Type |
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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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 |
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Title |
Stacked BCDU-net with semantic CMR synthesis: application to Myocardial Pathology Segmentation challenge |
Type |
Conference Article |
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Year |
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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Call Number |
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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no |
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Admin @ si @ AAF2020 |
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3520 |
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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 |
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Title |
Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review |
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Journal Article |
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Year |
2020 |
Publication |
IET Biometrics |
Abbreviated Journal |
BIO |
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10 |
Issue |
1 |
Pages |
24-43 |
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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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HUPBA; no proj |
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no |
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Call Number |
Admin @ si @ LLW2020b |
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3523 |
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Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
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Title |
Hand pose aware multimodal isolated sign language recognition |
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Journal Article |
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Year |
2020 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
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80 |
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127–163 |
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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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HUPBA; no menciona |
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no |
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Admin @ si @ RKE2020 |
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3524 |
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Author |
Eduardo Aguilar; Petia Radeva |
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Title |
Uncertainty-aware integration of local and flat classifiers for food recognition |
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Journal Article |
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2020 |
Publication |
Pattern Recognition Letters |
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PRL |
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136 |
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237-243 |
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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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MILAB; no proj |
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Admin @ si @ AgR2020 |
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3525 |
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Author |
Petia Radeva |
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Title |
Uncertainty Modeling within an End-to-end Framework for Food Image Analysis |
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Conference Article |
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2020 |
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1st DELTA |
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DELTA |
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MILAB |
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Admin @ si @ Rad2020 |
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3527 |
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Author |
Martin Menchon; Estefania Talavera; Jose M. Massa; Petia Radeva |
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Title |
Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams |
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Conference Article |
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2020 |
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ECCV Workshops |
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12538 |
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469-484 |
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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. |
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Virtual; August 2020 |
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ECCVW |
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MILAB; no proj |
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Admin @ si @ MTM2020 |
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3528 |
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Author |
Mariona Caros; Maite Garolera; Petia Radeva; Xavier Giro |
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Automatic Reminiscence Therapy for Dementia |
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2020 |
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10th ACM International Conference on Multimedia Retrieval |
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383-387 |
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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. |
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Virtual; October 2020 |
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ICRM |
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no |
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Admin @ si @ CGR2020 |
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3529 |
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