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| Author | Minesh Mathew; Dimosthenis Karatzas; C.V. Jawahar | ||||
| Title | DocVQA: A Dataset for VQA on Document Images | Type | Conference Article | ||
| Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 2200-2209 | ||
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| Abstract | We present a new dataset for Visual Question Answering (VQA) on document images called DocVQA. The dataset consists of 50,000 questions defined on 12,000+ document images. Detailed analysis of the dataset in comparison with similar datasets for VQA and reading comprehension is presented. We report several baseline results by adopting existing VQA and reading comprehension models. Although the existing models perform reasonably well on certain types of questions, there is large performance gap compared to human performance (94.36% accuracy). The models need to improve specifically on questions where understanding structure of the document is crucial. The dataset, code and leaderboard are available at docvqa. org | ||||
| Address | Virtual; January 2021 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ MKJ2021 | Serial | 3498 | ||
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| Author | Tomas Sixta; Julio C. S. Jacques Junior; Pau Buch Cardona; Eduard Vazquez; Sergio Escalera | ||||
| Title | FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition | Type | Conference Article | ||
| Year | 2020 | Publication | ECCV Workshops | Abbreviated Journal | |
| Volume | 12540 | Issue | Pages | 463-481 | |
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| Abstract | This work summarizes the 2020 ChaLearn Looking at People Fair Face Recognition and Analysis Challenge and provides a description of the top-winning solutions and analysis of the results. The aim of the challenge was to evaluate accuracy and bias in gender and skin colour of submitted algorithms on the task of 1:1 face verification in the presence of other confounding attributes. Participants were evaluated using an in-the-wild dataset based on reannotated IJB-C, further enriched 12.5K new images and additional labels. The dataset is not balanced, which simulates a real world scenario where AI-based models supposed to present fair outcomes are trained and evaluated on imbalanced data. The challenge attracted 151 participants, who made more 1.8K submissions in total. The final phase of the challenge attracted 36 active teams out of which 10 exceeded 0.999 AUC-ROC while achieving very low scores in the proposed bias metrics. Common strategies by the participants were face pre-processing, homogenization of data distributions, the use of bias aware loss functions and ensemble models. The analysis of top-10 teams shows higher false positive rates (and lower false negative rates) for females with dark skin tone as well as the potential of eyeglasses and young age to increase the false positive rates too. | ||||
| 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 | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ SJB2020 | Serial | 3499 | ||
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| Author | Zhengying Liu; Zhen Xu; Shangeth Rajaa; Meysam Madadi; Julio C. S. Jacques Junior; Sergio Escalera; Adrien Pavao; Sebastien Treguer; Wei-Wei Tu; Isabelle Guyon | ||||
| Title | Towards Automated Deep Learning: Analysis of the AutoDL challenge series 2019 | Type | Conference Article | ||
| Year | 2020 | Publication | Proceedings of Machine Learning Research | Abbreviated Journal | |
| Volume | 123 | Issue | Pages | 242-252 | |
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| Abstract | We present the design and results of recent competitions in Automated Deep Learning (AutoDL). In the AutoDL challenge series 2019, we organized 5 machine learning challenges: AutoCV, AutoCV2, AutoNLP, AutoSpeech and AutoDL. The first 4 challenges concern each a specific application domain, such as computer vision, natural language processing and speech recognition. At the time of March 2020, the last challenge AutoDL is still on-going and we only present its design. Some highlights of this work include: (1) a benchmark suite of baseline AutoML solutions, with emphasis on domains for which Deep Learning methods have had prior success (image, video, text, speech, etc); (2) a novel any-time learning framework, which opens doors for further theoretical consideration; (3) a repository of around 100 datasets (from all above domains) over half of which are released as public datasets to enable research on meta-learning; (4) analyses revealing that winning solutions generalize to new unseen datasets, validating progress towards universal AutoML solution; (5) open-sourcing of the challenge platform, the starting kit, the dataset formatting toolkit, and all winning solutions (All information available at {autodl.chalearn.org}). | ||||
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| Area | Expedition | Conference | NEURIPS | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ LXR2020 | Serial | 3500 | ||
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| Author | Albert Clapes; Julio C. S. Jacques Junior; Carla Morral; Sergio Escalera | ||||
| Title | ChaLearn LAP 2020 Challenge on Identity-preserved Human Detection: Dataset and Results | Type | Conference Article | ||
| Year | 2020 | Publication | 15th IEEE International Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 801-808 | ||
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| Abstract | This paper summarizes the ChaLearn Looking at People 2020 Challenge on Identity-preserved Human Detection (IPHD). For the purpose, we released a large novel dataset containing more than 112K pairs of spatiotemporally aligned depth and thermal frames (and 175K instances of humans) sampled from 780 sequences. The sequences contain hundreds of non-identifiable people appearing in a mix of in-the-wild and scripted scenarios recorded in public and private places. The competition was divided into three tracks depending on the modalities exploited for the detection: (1) depth, (2) thermal, and (3) depth-thermal fusion. Color was also captured but only used to facilitate the groundtruth annotation. Still the temporal synchronization of three sensory devices is challenging, so bad temporal matches across modalities can occur. Hence, the labels provided should considered “weak”, although test frames were carefully selected to minimize this effect and ensure the fairest comparison of the participants’ results. Despite this added difficulty, the results got by the participants demonstrate current fully-supervised methods can deal with that and achieve outstanding detection performance when measured in terms of AP@0.50. | ||||
| Address | Virtual; November 2020 | ||||
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| Area | Expedition | Conference | FG | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ CJM2020 | Serial | 3501 | ||
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| Author | Zhengying Liu; Adrien Pavao; Zhen Xu; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Sebastien Treguer | ||||
| Title | How far are we from true AutoML: reflection from winning solutions and results of AutoDL challenge | Type | Conference Article | ||
| Year | 2020 | Publication | 7th ICML Workshop on Automated Machine Learning | Abbreviated Journal | |
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| Abstract | Following the completion of the AutoDL challenge (the final challenge in the ChaLearn
AutoDL challenge series 2019), we investigate winning solutions and challenge results to answer an important motivational question: how far are we from achieving true AutoML? On one hand, the winning solutions achieve good (accurate and fast) classification performance on unseen datasets. On the other hand, all winning solutions still contain a considerable amount of hard-coded knowledge on the domain (or modality) such as image, video, text, speech and tabular. This form of ad-hoc meta-learning could be replaced by more automated forms of meta-learning in the future. Organizing a meta-learning challenge could help forging AutoML solutions that generalize to new unseen domains (e.g. new types of sensor data) as well as gaining insights on the AutoML problem from a more fundamental point of view. The datasets of the AutoDL challenge are a resource that can be used for further benchmarks and the code of the winners has been outsourced, which is a big step towards “democratizing” Deep Learning. |
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| Address | Virtual; July 2020 | ||||
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| Area | Expedition | Conference | ICML | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ LPX2020 | Serial | 3502 | ||
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| Author | Aymen Azaza; Joost Van de Weijer; Ali Douik; Javad Zolfaghari Bengar; Marc Masana | ||||
| Title | Saliency from High-Level Semantic Image Features | Type | Journal | ||
| Year | 2020 | Publication | SN Computer Science | Abbreviated Journal | SN |
| Volume | 1 | Issue | 4 | Pages | 1-12 |
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| Abstract | Top-down semantic information is known to play an important role in assigning saliency. Recently, large strides have been made in improving state-of-the-art semantic image understanding in the fields of object detection and semantic segmentation. Therefore, since these methods have now reached a high-level of maturity, evaluation of the impact of high-level image understanding on saliency estimation is now feasible. We propose several saliency features which are computed from object detection and semantic segmentation results. We combine these features with a standard baseline method for saliency detection to evaluate their importance. Experiments demonstrate that the proposed features derived from object detection and semantic segmentation improve saliency estimation significantly. Moreover, they show that our method obtains state-of-the-art results on (FT, ImgSal, and SOD datasets) and obtains competitive results on four other datasets (ECSSD, PASCAL-S, MSRA-B, and HKU-IS). | ||||
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| Notes | LAMP; 600.120; 600.109; 600.106 | Approved | no | ||
| Call Number | Admin @ si @ AWD2020 | Serial | 3503 | ||
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| Author | Rahma Kalboussi; Aymen Azaza; Joost Van de Weijer; Mehrez Abdellaoui; Ali Douik | ||||
| Title | Object proposals for salient object segmentation in videos | Type | Journal Article | ||
| Year | 2020 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 79 | Issue | 13 | Pages | 8677-8693 |
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| Abstract | Salient object segmentation in videos is generally broken up in a video segmentation part and a saliency assignment part. Recently, object proposals, which are used to segment the image, have had significant impact on many computer vision applications, including image segmentation, object detection, and recently saliency detection in still images. However, their usage has not yet been evaluated for salient object segmentation in videos. Therefore, in this paper, we investigate the application of object proposals to salient object segmentation in videos. In addition, we propose a new motion feature derived from the optical flow structure tensor for video saliency detection. Experiments on two standard benchmark datasets for video saliency show that the proposed motion feature improves saliency estimation results, and that object proposals are an efficient method for salient object segmentation. Results on the challenging SegTrack v2 and Fukuchi benchmark data sets show that we significantly outperform the state-of-the-art. | ||||
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| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | KAW2020 | Serial | 3504 | ||
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| Author | Marc Masana; Bartlomiej Twardowski; Joost Van de Weijer | ||||
| Title | On Class Orderings for Incremental Learning | Type | Conference Article | ||
| Year | 2020 | Publication | ICML Workshop on Continual Learning | Abbreviated Journal | |
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| Abstract | The influence of class orderings in the evaluation of incremental learning has received very little attention. In this paper, we investigate the impact of class orderings for incrementally learned classifiers. We propose a method to compute various orderings for a dataset. The orderings are derived by simulated annealing optimization from the confusion matrix and reflect different incremental learning scenarios, including maximally and minimally confusing tasks. We evaluate a wide range of state-of-the-art incremental learning methods on the proposed orderings. Results show that orderings can have a significant impact on performance and the ranking of the methods. | ||||
| Address | Virtual; July 2020 | ||||
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| Area | Expedition | Conference | ICMLW | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ MTW2020 | Serial | 3505 | ||
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| Author | David Berga; Marc Masana; Joost Van de Weijer | ||||
| Title | Disentanglement of Color and Shape Representations for Continual Learning | Type | Conference Article | ||
| Year | 2020 | Publication | ICML Workshop on Continual Learning | Abbreviated Journal | |
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| Abstract | 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. | ||||
| Address | Virtual; July 2020 | ||||
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| Area | Expedition | Conference | ICMLW | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ BMW2020 | Serial | 3506 | ||
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| Author | Diana Ramirez Cifuentes; Ana Freire; Ricardo Baeza Yates; Joaquim Punti Vidal; Pilar Medina Bravo; Diego Velazquez; Josep M. Gonfaus; Jordi Gonzalez | ||||
| Title | Detection of Suicidal Ideation on Social Media: Multimodal, Relational, and Behavioral Analysis | Type | Journal Article | ||
| Year | 2020 | Publication | Journal of Medical Internet Research | Abbreviated Journal | JMIR |
| Volume | 22 | Issue | 7 | Pages | e17758 |
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| Abstract | Background:
Suicide risk assessment usually involves an interaction between doctors and patients. However, a significant number of people with mental disorders receive no treatment for their condition due to the limited access to mental health care facilities; the reduced availability of clinicians; the lack of awareness; and stigma, neglect, and discrimination surrounding mental disorders. In contrast, internet access and social media usage have increased significantly, providing experts and patients with a means of communication that may contribute to the development of methods to detect mental health issues among social media users. Objective: This paper aimed to describe an approach for the suicide risk assessment of Spanish-speaking users on social media. We aimed to explore behavioral, relational, and multimodal data extracted from multiple social platforms and develop machine learning models to detect users at risk. Methods: We characterized users based on their writings, posting patterns, relations with other users, and images posted. We also evaluated statistical and deep learning approaches to handle multimodal data for the detection of users with signs of suicidal ideation (suicidal ideation risk group). Our methods were evaluated over a dataset of 252 users annotated by clinicians. To evaluate the performance of our models, we distinguished 2 control groups: users who make use of suicide-related vocabulary (focused control group) and generic random users (generic control group). Results: We identified significant statistical differences between the textual and behavioral attributes of each of the control groups compared with the suicidal ideation risk group. At a 95% CI, when comparing the suicidal ideation risk group and the focused control group, the number of friends (P=.04) and median tweet length (P=.04) were significantly different. The median number of friends for a focused control user (median 578.5) was higher than that for a user at risk (median 372.0). Similarly, the median tweet length was higher for focused control users, with 16 words against 13 words of suicidal ideation risk users. Our findings also show that the combination of textual, visual, relational, and behavioral data outperforms the accuracy of using each modality separately. We defined text-based baseline models based on bag of words and word embeddings, which were outperformed by our models, obtaining an increase in accuracy of up to 8% when distinguishing users at risk from both types of control users. Conclusions: The types of attributes analyzed are significant for detecting users at risk, and their combination outperforms the results provided by generic, exclusively text-based baseline models. After evaluating the contribution of image-based predictive models, we believe that our results can be improved by enhancing the models based on textual and relational features. These methods can be extended and applied to different use cases related to other mental disorders. |
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| Notes | ISE; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ RFB2020 | Serial | 3552 | ||
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| Author | Asma Bensalah; Jialuo Chen; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados; Miguel A. Ferrer | ||||
| Title | Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches. | Type | Conference Article | ||
| Year | 2020 | Publication | International Workshop on Artificial Intelligence for Healthcare Applications | Abbreviated Journal | |
| Volume | 12661 | Issue | Pages | 476-489 | |
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| Abstract | 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. | ||||
| Address | Virtual; January 2021 | ||||
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| Area | Expedition | Conference | ICPRW | ||
| Notes | DAG; 600.121; 600.140; | Approved | no | ||
| Call Number | Admin @ si @ BCF2020 | Serial | 3508 | ||
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| Author | Manuel Carbonell; Pau Riba; Mauricio Villegas; Alicia Fornes; Josep Llados | ||||
| Title | Named Entity Recognition and Relation Extraction with Graph Neural Networks in Semi Structured Documents | Type | Conference Article | ||
| Year | 2020 | Publication | 25th International Conference on Pattern Recognition | Abbreviated Journal | |
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| Abstract | 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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| Address | Virtual; January 2021 | ||||
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| Area | Expedition | Conference | ICPR | ||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ CRV2020 | Serial | 3509 | ||
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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 | ||
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| 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 | ||||
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| Area | Expedition | Conference | ICPR | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ LLW2020a | Serial | 3511 | ||
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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 | ||
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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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| Address | Virtual; November 2020 | ||||
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| 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 | ||
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| Address | Virtual; November 2020 | ||||
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| Area | Expedition | Conference | FGW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ EAM2020 | Serial | 3515 | ||
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