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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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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICPR | ||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ CRV2020 | Serial  |
3509 | ||
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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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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICPRW | ||
| Notes | DAG; 600.121; 600.140; | Approved | no | ||
| Call Number | Admin @ si @ BCF2020 | Serial |
3508 | ||
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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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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICMLW | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ BMW2020 | Serial |
3506 | ||
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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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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICMLW | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ MTW2020 | Serial |
3505 | ||
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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 | 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 | 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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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | ICML | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ LPX2020 | Serial |
3502 | ||
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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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| Language | Summary Language | Original Title | |||
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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; 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 | 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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| Language | Summary Language | Original Title | |||
| 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 | Raul Gomez; Yahui Liu; Marco de Nadai; Dimosthenis Karatzas; Bruno Lepri; Nicu Sebe | ||||
| Title | Retrieval Guided Unsupervised Multi-domain Image to Image Translation | Type | Conference Article | ||
| Year | 2020 | Publication | 28th ACM International Conference on Multimedia | Abbreviated Journal | |
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| Abstract | Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. Recent works assume that images descriptors can be disentangled into a domain-invariant content representation and a domain-specific style representation. Thus, translation models seek to preserve the content of source images while changing the style to a target visual domain. However, synthesizing new images is extremely challenging especially in multi-domain translations, as the network has to compose content and style to generate reliable and diverse images in multiple domains. In this paper we propose the use of an image retrieval system to assist the image-to-image translation task. First, we train an image-to-image translation model to map images to multiple domains. Then, we train an image retrieval model using real and generated images to find images similar to a query one in content but in a different domain. Finally, we exploit the image retrieval system to fine-tune the image-to-image translation model and generate higher quality images. Our experiments show the effectiveness of the proposed solution and highlight the contribution of the retrieval network, which can benefit from additional unlabeled data and help image-to-image translation models in the presence of scarce data. | ||||
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| Area | Expedition | Conference | ACM | ||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GLN2020 | Serial |
3497 | ||
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| Author | Jon Almazan; Lluis Gomez; Suman Ghosh; Ernest Valveny; Dimosthenis Karatzas | ||||
| Title | WATTS: A common representation of word images and strings using embedded attributes for text recognition and retrieval | Type | Book Chapter | ||
| Year | 2020 | Publication | Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis | Abbreviated Journal | |
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| Publisher | Springer | Place of Publication | Editor | Analysis”, K. Alahari; C.V. Jawahar | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Series on Advances in Computer Vision and Pattern Recognition | Abbreviated Series Title | ||
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| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ AGG2020 | Serial |
3496 | ||
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| Author | Lluis Gomez; Dena Bazazian; Dimosthenis Karatzas | ||||
| Title | Historical review of scene text detection research | Type | Book Chapter | ||
| Year | 2020 | Publication | Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis | Abbreviated Journal | |
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| Publisher | Springer | Place of Publication | Editor | K. Alahari; C.V. Jawahar | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Series on Advances in Computer Vision and Pattern Recognition | Abbreviated Series Title | ||
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| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GBK2020 | Serial |
3495 | ||
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| Author | Lluis Gomez; Anguelos Nicolaou; Marçal Rusiñol; Dimosthenis Karatzas | ||||
| Title | 12 years of ICDAR Robust Reading Competitions: The evolution of reading systems for unconstrained text understanding | Type | Book Chapter | ||
| Year | 2020 | Publication | Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis | Abbreviated Journal | |
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| Publisher | Springer | Place of Publication | Editor | K. Alahari; C.V. Jawahar | |
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| Series Editor | Series Title | Series on Advances in Computer Vision and Pattern Recognition | Abbreviated Series Title | ||
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| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | GNR2020 | Serial |
3494 | ||
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| Author | Yi Xiao; Felipe Codevilla; Akhil Gurram; Onay Urfalioglu; Antonio Lopez | ||||
| Title | Multimodal end-to-end autonomous driving | Type | Journal Article | ||
| Year | 2020 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
| Volume | Issue | Pages | 1-11 | ||
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| Abstract | A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to drive towards a desired destination. Today, there are different paradigms addressing the development of AI drivers. On the one hand, we find modular pipelines, which divide the driving task into sub-tasks such as perception and maneuver planning and control. On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals. The later are relatively less studied, but are gaining popularity since they are less demanding in terms of sensor data annotation. This paper focuses on end-to-end autonomous driving. So far, most proposals relying on this paradigm assume RGB images as input sensor data. However, AVs will not be equipped only with cameras, but also with active sensors providing accurate depth information (e.g., LiDARs). Accordingly, this paper analyses whether combining RGB and depth modalities, i.e. using RGBD data, produces better end-to-end AI drivers than relying on a single modality. We consider multimodality based on early, mid and late fusion schemes, both in multisensory and single-sensor (monocular depth estimation) settings. Using the CARLA simulator and conditional imitation learning (CIL), we show how, indeed, early fusion multimodality outperforms single-modality. | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ XCG2020 | Serial |
3490 | ||
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