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Author | Sergio Escalera; Markus Weimer; Mikhail Burtsev; Valentin Malykh; Varvara Logacheva; Ryan Lowe; Iulian Vlad Serban; Yoshua Bengio; Alexander Rudnicky; Alan W. Black; Shrimai Prabhumoye; Łukasz Kidzinski; Mohanty Sharada; Carmichael Ong; Jennifer Hicks; Sergey Levine; Marcel Salathe; Scott Delp; Iker Huerga; Alexander Grigorenko; Leifur Thorbergsson; Anasuya Das; Kyla Nemitz; Jenna Sandker; Stephen King; Alexander S. Ecker; Leon A. Gatys; Matthias Bethge; Jordan Boyd Graber; Shi Feng; Pedro Rodriguez; Mohit Iyyer; He He; Hal Daume III; Sean McGregor; Amir Banifatemi; Alexey Kurakin; Ian Goodfellow; Samy Bengio | ||||
Title | Introduction to NIPS 2017 Competition Track | Type | Book Chapter | ||
Year | 2018 | Publication | The NIPS ’17 Competition: Building Intelligent Systems | Abbreviated Journal | |
Volume | Issue | Pages | 1-23 | ||
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Abstract | Competitions have become a popular tool in the data science community to solve hard problems, assess the state of the art and spur new research directions. Companies like Kaggle and open source platforms like Codalab connect people with data and a data science problem to those with the skills and means to solve it. Hence, the question arises: What, if anything, could NIPS add to this rich ecosystem?
In 2017, we embarked to find out. We attracted 23 potential competitions, of which we selected five to be NIPS 2017 competitions. Our final selection features competitions advancing the state of the art in other sciences such as “Classifying Clinically Actionable Genetic Mutations” and “Learning to Run”. Others, like “The Conversational Intelligence Challenge” and “Adversarial Attacks and Defences” generated new data sets that we expect to impact the progress in their respective communities for years to come. And “Human-Computer Question Answering Competition” showed us just how far we as a field have come in ability and efficiency since the break-through performance of Watson in Jeopardy. Two additional competitions, DeepArt and AI XPRIZE Milestions, were also associated to the NIPS 2017 competition track, whose results are also presented within this chapter. |
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Publisher | Springer | Place of Publication | Editor | Sergio Escalera; Markus Weimer | |
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ISSN | ISBN | 978-3-319-94042-7 | Medium | ||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ EWB2018 | Serial | 3200 | ||
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Author | Pau Riba; Lutz Goldmann; Oriol Ramos Terrades; Diede Rusticus; Alicia Fornes; Josep Llados | ||||
Title | Table detection in business document images by message passing networks | Type | Journal Article | ||
Year | 2022 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 127 | Issue | Pages | 108641 | |
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Abstract | Tabular structures in business documents offer a complementary dimension to the raw textual data. For instance, there is information about the relationships among pieces of information. Nowadays, digital mailroom applications have become a key service for workflow automation. Therefore, the detection and interpretation of tables is crucial. With the recent advances in information extraction, table detection and recognition has gained interest in document image analysis, in particular, with the absence of rule lines and unknown information about rows and columns. However, business documents usually contain sensitive contents limiting the amount of public benchmarking datasets. In this paper, we propose a graph-based approach for detecting tables in document images which do not require the raw content of the document. Hence, the sensitive content can be previously removed and, instead of using the raw image or textual content, we propose a purely structural approach to keep sensitive data anonymous. Our framework uses graph neural networks (GNNs) to describe the local repetitive structures that constitute a table. In particular, our main application domain are business documents. We have carefully validated our approach in two invoice datasets and a modern document benchmark. Our experiments demonstrate that tables can be detected by purely structural approaches. | ||||
Address | July 2022 | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
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Notes | DAG; 600.162; 600.121 | Approved | no | ||
Call Number | Admin @ si @ RGR2022 | Serial | 3729 | ||
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Author | Meysam Madadi; Sergio Escalera; Alex Carruesco Llorens; Carlos Andujar; Xavier Baro; Jordi Gonzalez | ||||
Title | Top-down model fitting for hand pose recovery in sequences of depth images | Type | Journal Article | ||
Year | 2018 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
Volume | 79 | Issue | Pages | 63-75 | |
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Abstract | State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. We evaluate our approach on a new created synthetic hand dataset along with NYU and MSRA real datasets. Results demonstrate that the proposed method outperforms the most recent pose recovering approaches, including those based on CNNs. | ||||
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Notes | HUPBA; 600.098 | Approved | no | ||
Call Number | Admin @ si @ MEC2018 | Serial | 3203 | ||
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Author | Giuseppe Pezzano; Oliver Diaz; Vicent Ribas Ripoll; Petia Radeva | ||||
Title | CoLe-CNN+: Context learning – Convolutional neural network for COVID-19-Ground-Glass-Opacities detection and segmentation | Type | Journal Article | ||
Year | 2021 | Publication | Computers in Biology and Medicine | Abbreviated Journal | CBM |
Volume | 136 | Issue | Pages | 104689 | |
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Abstract | The most common tool for population-wide COVID-19 identification is the Reverse Transcription-Polymerase Chain Reaction test that detects the presence of the virus in the throat (or sputum) in swab samples. This test has a sensitivity between 59% and 71%. However, this test does not provide precise information regarding the extension of the pulmonary infection. Moreover, it has been proven that through the reading of a computed tomography (CT) scan, a clinician can provide a more complete perspective of the severity of the disease. Therefore, we propose a comprehensive system for fully-automated COVID-19 detection and lesion segmentation from CT scans, powered by deep learning strategies to support decision-making process for the diagnosis of COVID-19. | ||||
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Notes | MILAB; no menciona | Approved | no | ||
Call Number | Admin @ si @ PDR2021 | Serial | 3635 | ||
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Author | Yagmur Gucluturk; Umut Guclu; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera; Marcel A. J. van Gerven; Rob van Lier | ||||
Title | Multimodal First Impression Analysis with Deep Residual Networks | Type | Journal Article | ||
Year | 2018 | Publication | IEEE Transactions on Affective Computing | Abbreviated Journal | TAC |
Volume | 8 | Issue | 3 | Pages | 316-329 |
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Abstract | People form first impressions about the personalities of unfamiliar individuals even after very brief interactions with them. In this study we present and evaluate several models that mimic this automatic social behavior. Specifically, we present several models trained on a large dataset of short YouTube video blog posts for predicting apparent Big Five personality traits of people and whether they seem suitable to be recommended to a job interview. Along with presenting our audiovisual approach and results that won the third place in the ChaLearn First Impressions Challenge, we investigate modeling in different modalities including audio only, visual only, language only, audiovisual, and combination of audiovisual and language. Our results demonstrate that the best performance could be obtained using a fusion of all data modalities. Finally, in order to promote explainability in machine learning and to provide an example for the upcoming ChaLearn challenges, we present a simple approach for explaining the predictions for job interview recommendations | ||||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ GGB2018 | Serial | 3210 | ||
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Author | Rain Eric Haamer; Eka Rusadze; Iiris Lusi; Tauseef Ahmed; Sergio Escalera; Gholamreza Anbarjafari | ||||
Title | Review on Emotion Recognition Databases | Type | Book Chapter | ||
Year | 2018 | Publication | Human-Robot Interaction: Theory and Application | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | emotion; computer vision; databases | ||||
Abstract | Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and informative tables based on the format. | ||||
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ISSN | ISBN | 978-1-78923-316-2 | Medium | ||
Area | Expedition | Conference ![]() |
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Notes | HUPBA; 602.133 | Approved | no | ||
Call Number | Admin @ si @ HRL2018 | Serial | 3212 | ||
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Author | Reza Azad; Maryam Asadi-Aghbolaghi; Shohreh Kasaei; Sergio Escalera | ||||
Title | Dynamic 3D Hand Gesture Recognition by Learning Weighted Depth Motion Maps | Type | Journal Article | ||
Year | 2019 | Publication | IEEE Transactions on Circuits and Systems for Video Technology | Abbreviated Journal | TCSVT |
Volume | 29 | Issue | 6 | Pages | 1729-1740 |
Keywords | Hand gesture recognition; Multilevel temporal sampling; Weighted depth motion map; Spatio-temporal description; VLAD encoding | ||||
Abstract | Hand gesture recognition from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of key-frames of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatio-temporal information from generated summarized sequences by an accumulated weighted absolute difference of consecutive frames. The histogram of gradient (HOG) and local binary pattern (LBP) are exploited to extract features from WDMM. The obtained results define the current state-of-the-art on three public benchmark datasets of: MSR Gesture 3D, SKIG, and MSR Action 3D, for 3D hand gesture recognition. We also achieve competitive results on NTU action dataset. | ||||
Address | June 2019, | ||||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ AAK2018 | Serial | 3213 | ||
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Author | Ester Fornells; Manuel De Armas; Maria Teresa Anguera; Sergio Escalera; Marcos Antonio Catalán; Josep Moya | ||||
Title | Desarrollo del proyecto del Consell Comarcal del Baix Llobregat “Buen Trato a las personas mayores y aquellas en situación de fragilidad con sufrimiento emocional: Hacia un envejecimiento saludable” | Type | Journal | ||
Year | 2018 | Publication | Informaciones Psiquiatricas | Abbreviated Journal | |
Volume | 232 | Issue | Pages | 47-59 | |
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ISSN | 0210-7279 | ISBN | Medium | ||
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Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ FAA2018 | Serial | 3214 | ||
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Author | Suman Ghosh | ||||
Title | Word Spotting and Recognition in Images from Heterogeneous Sources A | Type | Book Whole | ||
Year | 2018 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Text is the most common way of information sharing from ages. With recent development of personal images databases and handwritten historic manuscripts the demand for algorithms to make these databases accessible for browsing and indexing are in rise. Enabling search or understanding large collection of manuscripts or image databases needs fast and robust methods. Researchers have found different ways to represent cropped words for understanding and matching, which works well when words are already segmented. However there is no trivial way to extend these for non-segmented documents. In this thesis we explore different methods for text retrieval and recognition from unsegmented document and scene images. Two different ways of representation exist in literature, one uses a fixed length representation learned from cropped words and another a sequence of features of variable length. Throughout this thesis, we have studied both these representation for their suitability in segmentation free understanding of text. In the first part we are focused on segmentation free word spotting using a fixed length representation. We extended the use of the successful PHOC (Pyramidal Histogram of Character) representation to segmentation free retrieval. In the second part of the thesis, we explore sequence based features and finally, we propose a unified solution where the same framework can generate both kind of representations. | ||||
Address | November 2018 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Ernest Valveny | |
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ISSN | ISBN | 978-84-948531-0-4 | Medium | ||
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Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ Gho2018 | Serial | 3217 | ||
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Author | Gholamreza Anbarjafari; Sergio Escalera | ||||
Title | Human-Robot Interaction: Theory and Application | Type | Book Whole | ||
Year | 2018 | Publication | Human-Robot Interaction: Theory and Application | Abbreviated Journal | |
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ISSN | ISBN | 978-1-78923-316-2 | Medium | ||
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Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ AnE2018 | Serial | 3216 | ||
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Author | Mikhail Mozerov; Joost Van de Weijer | ||||
Title | One-view occlusion detection for stereo matching with a fully connected CRF model | Type | Journal Article | ||
Year | 2019 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 28 | Issue | 6 | Pages | 2936-2947 |
Keywords | Stereo matching; energy minimization; fully connected MRF model; geodesic distance filter | ||||
Abstract | In this paper, we extend the standard belief propagation (BP) sequential technique proposed in the tree-reweighted sequential method [15] to the fully connected CRF models with the geodesic distance affinity. The proposed method has been applied to the stereo matching problem. Also a new approach to the BP marginal solution is proposed that we call one-view occlusion detection (OVOD). In contrast to the standard winner takes all (WTA) estimation, the proposed OVOD solution allows to find occluded regions in the disparity map and simultaneously improve the matching result. As a result we can perform only
one energy minimization process and avoid the cost calculation for the second view and the left-right check procedure. We show that the OVOD approach considerably improves results for cost augmentation and energy minimization techniques in comparison with the standard one-view affinity space implementation. We apply our method to the Middlebury data set and reach state-ofthe-art especially for median, average and mean squared error metrics. |
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Notes | LAMP; 600.098; 600.109; 602.133; 600.120 | Approved | no | ||
Call Number | Admin @ si @ MoW2019 | Serial | 3221 | ||
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Author | Anjan Dutta; Hichem Sahbi | ||||
Title | Stochastic Graphlet Embedding | Type | Journal Article | ||
Year | 2018 | Publication | IEEE Transactions on Neural Networks and Learning Systems | Abbreviated Journal | TNNLS |
Volume | Issue | Pages | 1-14 | ||
Keywords | Stochastic graphlets; Graph embedding; Graph classification; Graph hashing; Betweenness centrality | ||||
Abstract | Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments,
etc.) and edges characterize the relationships between these primitives. However, these non-vectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of – explicit/implicit –graph vectorization and embedding. This embedding process should be resilient to intra-class graph variations while being highly discriminant. In this paper, we propose a novel high-order stochastic graphlet embedding (SGE) that maps graphs into vector spaces. Our main contribution includes a new stochastic search procedure that efficiently parses a given graph and extracts/samples unlimitedly high-order graphlets. We consider these graphlets, with increasing orders, to model local primitives as well as their increasingly complex interactions. In order to build our graph representation, we measure the distribution of these graphlets into a given graph, using particular hash functions that efficiently assign sampled graphlets into isomorphic sets with a very low probability of collision. When combined with maximum margin classifiers, these graphlet-based representations have positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases. |
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Notes | DAG; 602.167; 602.168; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ DuS2018 | Serial | 3225 | ||
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Author | Xim Cerda-Company; Xavier Otazu | ||||
Title | Color induction in equiluminant flashed stimuli | Type | Journal Article | ||
Year | 2019 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A |
Volume | 36 | Issue | 1 | Pages | 22-31 |
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Abstract | Color induction is the influence of the surrounding color (inducer) on the perceived color of a central region. There are two different types of color induction: color contrast (the color of the central region shifts away from that of the inducer) and color assimilation (the color shifts towards the color of the inducer). Several studies on these effects have used uniform and striped surrounds, reporting color contrast and color assimilation, respectively. Other authors [J. Vis. 12(1), 22 (2012) [CrossRef] ] have studied color induction using flashed uniform surrounds, reporting that the contrast is higher for shorter flash duration. Extending their study, we present new psychophysical results using both flashed and static (i.e., non-flashed) equiluminant stimuli for both striped and uniform surrounds. Similarly to them, for uniform surround stimuli we observed color contrast, but we did not obtain the maximum contrast for the shortest (10 ms) flashed stimuli, but for 40 ms. We only observed this maximum contrast for red, green, and lime inducers, while for a purple inducer we obtained an asymptotic profile along the flash duration. For striped stimuli, we observed color assimilation only for the static (infinite flash duration) red–green surround inducers (red first inducer, green second inducer). For the other inducers’ configurations, we observed color contrast or no induction. Since other studies showed that non-equiluminant striped static stimuli induce color assimilation, our results also suggest that luminance differences could be a key factor to induce it. | ||||
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Notes | NEUROBIT; 600.120; 600.128 | Approved | no | ||
Call Number | Admin @ si @ CeO2019 | Serial | 3226 | ||
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Author | Lichao Zhang; Abel Gonzalez-Garcia; Joost Van de Weijer; Martin Danelljan; Fahad Shahbaz Khan | ||||
Title | Synthetic Data Generation for End-to-End Thermal Infrared Tracking | Type | Journal Article | ||
Year | 2019 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 28 | Issue | 4 | Pages | 1837 - 1850 |
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Abstract | The usage of both off-the-shelf and end-to-end trained deep networks have significantly improved the performance of visual tracking on RGB videos. However, the lack of large labeled datasets hampers the usage of convolutional neural networks for tracking in thermal infrared (TIR) images. Therefore, most state-of-the-art methods on tracking for TIR data are still based on handcrafted features. To address this problem, we propose to use image-to-image translation models. These models allow us to translate the abundantly available labeled RGB data to synthetic TIR data. We explore both the usage of paired and unpaired image translation models for this purpose. These methods provide us with a large labeled dataset of synthetic TIR sequences, on which we can train end-to-end optimal features for tracking. To the best of our knowledge, we are the first to train end-to-end features for TIR tracking. We perform extensive experiments on the VOT-TIR2017 dataset. We show that a network trained on a large dataset of synthetic TIR data obtains better performance than one trained on the available real TIR data. Combining both data sources leads to further improvement. In addition, when we combine the network with motion features, we outperform the state of the art with a relative gain of over 10%, clearly showing the efficiency of using synthetic data to train end-to-end TIR trackers. | ||||
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Notes | LAMP; 600.141; 600.120 | Approved | no | ||
Call Number | Admin @ si @ YGW2019 | Serial | 3228 | ||
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Author | Antonio Lopez | ||||
Title | Pedestrian Detection Systems | Type | Book Chapter | ||
Year | 2018 | Publication | Wiley Encyclopedia of Electrical and Electronics Engineering | Abbreviated Journal | |
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Abstract | Pedestrian detection is a highly relevant topic for both advanced driver assistance systems (ADAS) and autonomous driving. In this entry, we review the ideas behind pedestrian detection systems from the point of view of perception based on computer vision and machine learning. | ||||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ Lop2018 | Serial | 3230 | ||
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