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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
Keywords
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.
Address
Corporate Author Thesis
Publisher Springer Place of Publication Editor Sergio Escalera; Markus Weimer
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title (up)
Series Volume Series Issue Edition
ISSN ISBN 978-3-319-94042-7 Medium
Area Expedition Conference
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ EWB2018 Serial 3200
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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.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title (up)
Series Volume Series Issue Edition
ISSN ISBN 978-1-78923-316-2 Medium
Area Expedition Conference
Notes HUPBA; 602.133 Approved no
Call Number Admin @ si @ HRL2018 Serial 3212
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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
Volume Issue Pages
Keywords
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.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title (up)
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ Lop2018 Serial 3230
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Author Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas
Title Self-Supervised Learning from Web Data for Multimodal Retrieval Type Book Chapter
Year 2019 Publication Multi-Modal Scene Understanding Book Abbreviated Journal
Volume Issue Pages 279-306
Keywords self-supervised learning; webly supervised learning; text embeddings; multimodal retrieval; multimodal embedding
Abstract Self-Supervised learning from multimodal image and text data allows deep neural networks to learn powerful features with no need of human annotated data. Web and Social Media platforms provide a virtually unlimited amount of this multimodal data. In this work we propose to exploit this free available data to learn a multimodal image and text embedding, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the proposed pipeline can learn from images with associated text without supervision and analyze the semantic structure of the learnt joint image and text embeddingspace. Weperformathoroughanalysisandperformancecomparisonoffivedifferentstateof the art text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text basedimageretrievaltask,andweclearlyoutperformstateoftheartintheMIRFlickrdatasetwhen training in the target data. Further, we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings.
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Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title (up)
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG; 600.129; 601.338; 601.310 Approved no
Call Number Admin @ si @ GGG2019 Serial 3266
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Author Sergio Escalera; Marti Soler; Stephane Ayache; Umut Guçlu; Jun Wan; Meysam Madadi; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon
Title ChaLearn Looking at People: Inpainting and Denoising Challenges Type Book Chapter
Year 2019 Publication The Springer Series on Challenges in Machine Learning Abbreviated Journal
Volume Issue Pages 23-44
Keywords
Abstract Dealing with incomplete information is a well studied problem in the context of machine learning and computational intelligence. However, in the context of computer vision, the problem has only been studied in specific scenarios (e.g., certain types of occlusions in specific types of images), although it is common to have incomplete information in visual data. This chapter describes the design of an academic competition focusing on inpainting of images and video sequences that was part of the competition program of WCCI2018 and had a satellite event collocated with ECCV2018. The ChaLearn Looking at People Inpainting Challenge aimed at advancing the state of the art on visual inpainting by promoting the development of methods for recovering missing and occluded information from images and video. Three tracks were proposed in which visual inpainting might be helpful but still challenging: human body pose estimation, text overlays removal and fingerprint denoising. This chapter describes the design of the challenge, which includes the release of three novel datasets, and the description of evaluation metrics, baselines and evaluation protocol. The results of the challenge are analyzed and discussed in detail and conclusions derived from this event are outlined.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title (up)
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HuPBA; no proj Approved no
Call Number Admin @ si @ ESA2019 Serial 3327
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Author Alicia Fornes; Josep Llados; Joana Maria Pujadas-Mora
Title Browsing of the Social Network of the Past: Information Extraction from Population Manuscript Images Type Book Chapter
Year 2020 Publication Handwritten Historical Document Analysis, Recognition, and Retrieval – State of the Art and Future Trends Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis
Publisher World Scientific Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title (up)
Series Volume Series Issue Edition
ISSN ISBN 978-981-120-323-7 Medium
Area Expedition Conference
Notes DAG; 600.140; 600.121 Approved no
Call Number Admin @ si @ FLP2020 Serial 3350
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Author Joana Maria Pujadas-Mora; Alicia Fornes; Josep Llados; Gabriel Brea-Martinez; Miquel Valls-Figols
Title The Baix Llobregat (BALL) Demographic Database, between Historical Demography and Computer Vision (nineteenth–twentieth centuries Type Book Chapter
Year 2019 Publication Nominative Data in Demographic Research in the East and the West: monograph Abbreviated Journal
Volume Issue Pages 29-61
Keywords
Abstract The Baix Llobregat (BALL) Demographic Database is an ongoing database project containing individual census data from the Catalan region of Baix Llobregat (Spain) during the nineteenth and twentieth centuries. The BALL Database is built within the project ‘NETWORKS: Technology and citizen innovation for building historical social networks to understand the demographic past’ directed by Alícia Fornés from the Center for Computer Vision and Joana Maria Pujadas-Mora from the Center for Demographic Studies, both at the Universitat Autònoma de Barcelona, funded by the Recercaixa program (2017–2019).
Its webpage is http://dag.cvc.uab.es/xarxes/.The aim of the project is to develop technologies facilitating massive digitalization of demographic sources, and more specifically the padrones (local censuses), in order to reconstruct historical ‘social’ networks employing computer vision technology. Such virtual networks can be created thanks to the linkage of nominative records compiled in the local censuses across time and space. Thus, digitized versions of individual and family lifespans are established, and individuals and families can be located spatially.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title (up)
Series Volume Series Issue Edition
ISSN ISBN 978-5-7996-2656-3 Medium
Area Expedition Conference
Notes DAG; 600.121 Approved no
Call Number Admin @ si @ PFL2019 Serial 3351
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Author Estefania Talavera; Alexandre Cola; Nicolai Petkov; Petia Radeva
Title Towards Egocentric Person Re-identification and Social Pattern Analysis. Type Book Chapter
Year 2019 Publication Frontiers in Artificial Intelligence and Applications Abbreviated Journal
Volume 310 Issue Pages 203 - 211
Keywords
Abstract CoRR abs/1905.04073
Wearable cameras capture a first-person view of the daily activities of the camera wearer, offering a visual diary of the user behaviour. Detection of the appearance of people the camera user interacts with for social interactions analysis is of high interest. Generally speaking, social events, lifestyle and health are highly correlated, but there is a lack of tools to monitor and analyse them. We consider that egocentric vision provides a tool to obtain information and understand users social interactions. We propose a model that enables us to evaluate and visualize social traits obtained by analysing social interactions appearance within egocentric photostreams. Given sets of egocentric images, we detect the appearance of faces within the days of the camera wearer, and rely on clustering algorithms to group their feature descriptors in order to re-identify persons. Recurrence of detected faces within photostreams allows us to shape an idea of the social pattern of behaviour of the user. We validated our model over several weeks recorded by different camera wearers. Our findings indicate that social profiles are potentially useful for social behaviour interpretation.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title (up)
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB; no proj Approved no
Call Number Admin @ si @ TCP2019 Serial 3377
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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
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis
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 (up)
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG; 600.121 Approved no
Call Number GNR2020 Serial 3494
Permanent link to this record
 
 
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
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis
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 (up)
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG; 600.121 Approved no
Call Number Admin @ si @ GBK2020 Serial 3495
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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
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis
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 (up)
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG; 600.121 Approved no
Call Number Admin @ si @ AGG2020 Serial 3496
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Author Patricia Suarez; Angel Sappa; Boris X. Vintimilla
Title Deep learning-based vegetation index estimation Type Book Chapter
Year 2021 Publication Generative Adversarial Networks for Image-to-Image Translation Abbreviated Journal
Volume Issue Pages 205-234
Keywords
Abstract Chapter 9
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor A.Solanki; A.Nayyar; M.Naved
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title (up)
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MSIAU; 600.122 Approved no
Call Number Admin @ si @ SSV2021a Serial 3578
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Author Debora Gil; Oriol Ramos Terrades; Raquel Perez
Title Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution Type Book Chapter
Year 2021 Publication Extended Abstracts GEOMVAP 2019, Trends in Mathematics 15 Abbreviated Journal
Volume 15 Issue Pages 89–93
Keywords
Abstract Abnormalities in radiomic measures correlate to genomic alterations prone to alter the outcome of personalized anti-cancer treatments. TOPiomics is a new method for the early detection of variations in tumor imaging phenotype from a topological structure in multi-view radiomic spaces.
Address
Corporate Author Thesis
Publisher Springer Nature Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title (up)
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes IAM; DAG; 600.120; 600.145; 600.139 Approved no
Call Number Admin @ si @ GRP2021 Serial 3594
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Author Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li
Title Best Solutions Proposed in the Context of the Face Anti-spoofing Challenge Series Type Book Chapter
Year 2023 Publication Advances in Face Presentation Attack Detection Abbreviated Journal
Volume Issue Pages 37–78
Keywords
Abstract The PAD competitions we organized attracted more than 835 teams from home and abroad, most of them from the industry, which shows that the topic of face anti-spoofing is closely related to daily life, and there is an urgent need for advanced algorithms to solve its application needs. Specifically, the Chalearn LAP multi-modal face anti-spoofing attack detection challenge attracted more than 300 teams for the development phase with a total of 13 teams qualifying for the final round; the Chalearn Face Anti-spoofing Attack Detection Challenge 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; the 3D High-Fidelity Mask Face Presentation Attack Detection Challenge attracted 195 teams for the development phase with a total of 18 teams qualifying for the final round. All the results were verified and re-run by the organizing team, and the results were used for the final ranking. In this chapter, we briefly the methods developed by the teams participating in each competition, and introduce the algorithm details of the top-three ranked teams in detail.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title (up)
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA Approved no
Call Number Admin @ si @ WGE2023d Serial 3958
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Author Beata Megyesi; Alicia Fornes; Nils Kopal; Benedek Lang
Title Historical Cryptology Type Book Chapter
Year 2024 Publication Learning and Experiencing Cryptography with CrypTool and SageMath Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Historical cryptology studies (original) encrypted manuscripts, often handwritten sources, produced in our history. These historical sources can be found in archives, often hidden without any indexing and therefore hard to locate. Once found they need to be digitized and turned into a machine-readable text format before they can be deciphered with computational methods. The focus of historical cryptology is not primarily the development of sophisticated algorithms for decipherment, but rather the entire process of analysis of the encrypted source from collection and digitization to transcription and decryption. The process also includes the interpretation and contextualization of the message set in its historical context. There are many challenges on the way, such as mistakes made by the scribe, errors made by the transcriber, damaged pages, handwriting styles that are difficult to interpret, historical languages from various time periods, and hidden underlying language of the message. Ciphertexts vary greatly in terms of their code system and symbol sets used with more or less distinguishable symbols. Ciphertexts can be embedded in clearly written text, or shorter or longer sequences of cleartext can be embedded in the ciphertext. The ciphers used mostly in historical times are substitutions (simple, homophonic, or polyphonic), with or without nomenclatures, encoded as digits or symbol sequences, with or without spaces. So the circumstances are different from those in modern cryptography which focuses on methods (algorithms) and their strengths and assumes that the algorithm is applied correctly. For both historical and modern cryptology, attack vectors outside the algorithm are applied like implementation flaws and side-channel attacks. In this chapter, we give an introduction to the field of historical cryptology and present an overview of how researchers today process historical encrypted sources.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title (up)
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG Approved no
Call Number Admin @ si @ MFK2024 Serial 4020
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