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	Author	Jaykishan Patel; Alban Flachot; Javier Vazquez; David H. Brainard; Thomas S. A. Wallis; Marcus A. Brubaker; Richard F. Murray
	Title	A deep convolutional neural network trained to infer surface reflectance is deceived by mid-level lightness illusions			Type	Journal Article
	Year	2023	Publication	Journal of Vision	Abbreviated Journal	JV
	Volume	23	Issue	9	Pages	4817-4817
	Keywords
	Abstract	A long-standing view is that lightness illusions are by-products of strategies employed by the visual system to stabilize its perceptual representation of surface reflectance against changes in illumination. Computationally, one such strategy is to infer reflectance from the retinal image, and to base the lightness percept on this inference. CNNs trained to infer reflectance from images have proven successful at solving this problem under limited conditions. To evaluate whether these CNNs provide suitable starting points for computational models of human lightness perception, we tested a state-of-the-art CNN on several lightness illusions, and compared its behaviour to prior measurements of human performance. We trained a CNN (Yu & Smith, 2019) to infer reflectance from luminance images. The network had a 30-layer hourglass architecture with skip connections. We trained the network via supervised learning on 100K images, rendered in Blender, each showing randomly placed geometric objects (surfaces, cubes, tori, etc.), with random Lambertian reflectance patterns (solid, Voronoi, or low-pass noise), under randomized point+ambient lighting. The renderer also provided the ground-truth reflectance images required for training. After training, we applied the network to several visual illusions. These included the argyle, Koffka-Adelson, snake, White’s, checkerboard assimilation, and simultaneous contrast illusions, along with their controls where appropriate. The CNN correctly predicted larger illusions in the argyle, Koffka-Adelson, and snake images than in their controls. It also correctly predicted an assimilation effect in White's illusion. It did not, however, account for the checkerboard assimilation or simultaneous contrast effects. These results are consistent with the view that at least some lightness phenomena are by-products of a rational approach to inferring stable representations of physical properties from intrinsically ambiguous retinal images. Furthermore, they suggest that CNN models may be a promising starting point for new models of human lightness perception.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MACO; CIC			Approved	no
	Call Number	Admin @ si @ PFV2023			Serial	3890
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	Author	Marco Pedersoli; Jordi Gonzalez; Andrew Bagdanov; Xavier Roca
	Title	Efficient Discriminative Multiresolution Cascade for Real-Time Human Detection Applications			Type	Journal Article
	Year	2011	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	32	Issue	13	Pages	1581-1587
	Keywords
	Abstract	Human detection is fundamental in many machine vision applications, like video surveillance, driving assistance, action recognition and scene understanding. However in most of these applications real-time performance is necessary and this is not achieved yet by current detection methods. This paper presents a new method for human detection based on a multiresolution cascade of Histograms of Oriented Gradients (HOG) that can highly reduce the computational cost of detection search without affecting accuracy. The method consists of a cascade of sliding window detectors. Each detector is a linear Support Vector Machine (SVM) composed of HOG features at different resolutions, from coarse at the first level to fine at the last one. In contrast to previous methods, our approach uses a non-uniform stride of the sliding window that is defined by the feature resolution and allows the detection to be incrementally refined as going from coarse-to-fine resolution. In this way, the speed-up of the cascade is not only due to the fewer number of features computed at the first levels of the cascade, but also to the reduced number of windows that need to be evaluated at the coarse resolution. Experimental results show that our method reaches a detection rate comparable with the state-of-the-art of detectors based on HOG features, while at the same time the detection search is up to 23 times faster.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ISE			Approved	no
	Call Number	Admin @ si @ PGB2011a			Serial	1707
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	Author	Victor Ponce; Mario Gorga; Xavier Baro; Sergio Escalera
	Title	Human Behavior Analysis from Video Data Using Bag-of-Gestures			Type	Conference Article
	Year	2011	Publication	22nd International Joint Conference on Artificial Intelligence	Abbreviated Journal
	Volume	3	Issue		Pages	2836-2837
	Keywords
	Abstract	Human Behavior Analysis in Uncontrolled Environments can be categorized in two main challenges: 1) Feature extraction and 2) Behavior analysis from a set of corporal language vocabulary. In this work, we present our achievements characterizing some simple behaviors from visual data on different real applications and discuss our plan for future work: low level vocabulary definition from bag-of-gesture units and high level modelling and inference of human behaviors.
	Address	Barcelona
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-1-57735-516-8	Medium
	Area		Expedition		Conference	IJCAI
	Notes	HuPBA;MV			Approved	no
	Call Number	Admin @ si @ PGB2011b			Serial	1770
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	Author	Victor Ponce; Mario Gorga; Xavier Baro; Petia Radeva; Sergio Escalera
	Title	Analisis de la Expresion Oral y Gestual en Proyectos Fin de Carrera Via un Sistema de Vision Artificial			Type	Miscellaneous
	Year	2011	Publication	Revista electronica de la asociacion de enseñantes universitarios de la informatica AENUI	Abbreviated Journal	ReVision
	Volume	4	Issue	1	Pages	8-18
	Keywords
	Abstract	La comunicación y expresión oral es una competencia de especial relevancia en el EEES. No obstante, en muchas enseñanzas superiores la puesta en práctica de esta competencia ha sido relegada principalmente a la presentación de proyectos fin de carrera. Dentro de un proyecto de innovación docente, se ha desarrollado una herramienta informática para la extracción de información objetiva para el análisis de la expresión oral y gestual de los alumnos. El objetivo es dar un “feedback” a los estudiantes que les permita mejorar la calidad de sus presentaciones. El prototipo inicial que se presenta en este trabajo permite extraer de forma automática información audiovisual y analizarla mediante técnicas de aprendizaje. El sistema ha sido aplicado a 15 proyectos fin de carrera y 15 exposiciones dentro de una asignatura de cuarto curso. Los resultados obtenidos muestran la viabilidad del sistema para sugerir factores que ayuden tanto en el éxito de la comunicación así como en los criterios de evaluación.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1989-1199	ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB;HuPBA;MV			Approved	no
	Call Number	Admin @ si @ PGB2011c			Serial	1783
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	Author	Victor Ponce; Mario Gorga; Xavier Baro; Petia Radeva; Sergio Escalera
	Title	Análisis de la expresión oral y gestual en proyectos fin de carrera vía un sistema de visión artificial			Type	Journal Article
	Year	2011	Publication	ReVisión	Abbreviated Journal
	Volume	4	Issue	1	Pages
	Keywords
	Abstract	La comunicación y expresión oral es una competencia de especial relevancia en el EEES. No obstante, en muchas enseñanzas superiores la puesta en práctica de esta competencia ha sido relegada principalmente a la presentación de proyectos fin de carrera. Dentro de un proyecto de innovación docente, se ha desarrollado una herramienta informática para la extracción de información objetiva para el análisis de la expresión oral y gestual de los alumnos. El objetivo es dar un “feedback” a los estudiantes que les permita mejorar la calidad de sus presentaciones. El prototipo inicial que se presenta en este trabajo permite extraer de forma automática información audiovisual y analizarla mediante técnicas de aprendizaje. El sistema ha sido aplicado a 15 proyectos fin de carrera y 15 exposiciones dentro de una asignatura de cuarto curso. Los resultados obtenidos muestran la viabilidad del sistema para sugerir factores que ayuden tanto en el éxito de la comunicación así como en los criterios de evaluación.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1989-1199	ISBN		Medium
	Area		Expedition		Conference
	Notes	HuPBA; MILAB;MV			Approved	no
	Call Number	Admin @ si @ PGB2011d			Serial	2514
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	Author	Y. Patel; Lluis Gomez; Raul Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar
	Title	TextTopicNet-Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces			Type	Miscellaneous
	Year	2018	Publication	Arxiv	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	The immense success of deep learning based methods in computer vision heavily relies on large scale training datasets. These richly annotated datasets help the network learn discriminative visual features. Collecting and annotating such datasets requires a tremendous amount of human effort and annotations are limited to popular set of classes. As an alternative, learning visual features by designing auxiliary tasks which make use of freely available self-supervision has become increasingly popular in the computer vision community. In this paper, we put forward an idea to take advantage of multi-modal context to provide self-supervision for the training of computer vision algorithms. We show that adequate visual features can be learned efficiently by training a CNN to predict the semantic textual context in which a particular image is more probable to appear as an illustration. More specifically we use popular text embedding techniques to provide the self-supervision for the training of deep CNN.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG; 600.084; 601.338; 600.121			Approved	no
	Call Number	Admin @ si @ PGG2018			Serial	3177
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	Author	Karel Paleček; David Geronimo; Frederic Lerasle
	Title	Pre-attention cues for person detection			Type	Conference Article
	Year	2012	Publication	Cognitive Behavioural Systems, COST 2102 International Training School	Abbreviated Journal
	Volume		Issue		Pages	225-235
	Keywords
	Abstract	Current state-of-the-art person detectors have been proven reliable and achieve very good detection rates. However, the performance is often far from real time, which limits their use to low resolution images only. In this paper, we deal with candidate window generation problem for person detection, i.e. we want to reduce the computational complexity of a person detector by reducing the number of regions that has to be evaluated. We base our work on Alexe’s paper [1], which introduced several pre-attention cues for generic object detection. We evaluate these cues in the context of person detection and show that their performance degrades rapidly for scenes containing multiple objects of interest such as pictures from urban environment. We extend this set by new cues, which better suits our class-specific task. The cues are designed to be simple and efficient, so that they can be used in the pre-attention phase of a more complex sliding window based person detector.
	Address	Dresden, Germany
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-34583-8	Medium
	Area		Expedition		Conference	COST-TS
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ PGL2012			Serial	2148
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	Author	Adrien Pavao; Isabelle Guyon; Anne-Catherine Letournel; Dinh-Tuan Tran; Xavier Baro; Hugo Jair Escalante; Sergio Escalera; Tyler Thomas; Zhen Xu
	Title	CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges			Type	Journal Article
	Year	2023	Publication	Journal of Machine Learning Research	Abbreviated Journal	JMLR
	Volume		Issue		Pages
	Keywords
	Abstract	CodaLab Competitions is an open source web platform designed to help data scientists and research teams to crowd-source the resolution of machine learning problems through the organization of competitions, also called challenges or contests. CodaLab Competitions provides useful features such as multiple phases, results and code submissions, multi-score leaderboards, and jobs running inside Docker containers. The platform is very flexible and can handle large scale experiments, by allowing organizers to upload large datasets and provide their own CPU or GPU compute workers.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	HUPBA			Approved	no
	Call Number	Admin @ si @ PGL2023			Serial	3973
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	Author	Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas
	Title	Dynamic Lexicon Generation for Natural Scene Images			Type	Conference Article
	Year	2016	Publication	14th European Conference on Computer Vision Workshops	Abbreviated Journal
	Volume		Issue		Pages	395-410
	Keywords	scene text; photo OCR; scene understanding; lexicon generation; topic modeling; CNN
	Abstract	Many scene text understanding methods approach the endtoend recognition problem from a word-spotting perspective and take huge benet from using small per-image lexicons. Such customized lexicons are normally assumed as given and their source is rarely discussed. In this paper we propose a method that generates contextualized lexicons for scene images using only visual information. For this, we exploit the correlation between visual and textual information in a dataset consisting of images and textual content associated with them. Using the topic modeling framework to discover a set of latent topics in such a dataset allows us to re-rank a xed dictionary in a way that prioritizes the words that are more likely to appear in a given image. Moreover, we train a CNN that is able to reproduce those word rankings but using only the image raw pixels as input. We demonstrate that the quality of the automatically obtained custom lexicons is superior to a generic frequency-based baseline.
	Address	Amsterdam; The Netherlands; October 2016
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ECCVW
	Notes	DAG; 600.084			Approved	no
	Call Number	Admin @ si @ PGR2016			Serial	2825
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	Author	Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar
	Title	Self-Supervised Visual Representations for Cross-Modal Retrieval			Type	Conference Article
	Year	2019	Publication	ACM International Conference on Multimedia Retrieval	Abbreviated Journal
	Volume		Issue		Pages	182–186
	Keywords
	Abstract	Cross-modal retrieval methods have been significantly improved in last years with the use of deep neural networks and large-scale annotated datasets such as ImageNet and Places. However, collecting and annotating such datasets requires a tremendous amount of human effort and, besides, their annotations are limited to discrete sets of popular visual classes that may not be representative of the richer semantics found on large-scale cross-modal retrieval datasets. In this paper, we present a self-supervised cross-modal retrieval framework that leverages as training data the correlations between images and text on the entire set of Wikipedia articles. Our method consists in training a CNN to predict: (1) the semantic context of the article in which an image is more probable to appear as an illustration, and (2) the semantic context of its caption. Our experiments demonstrate that the proposed method is not only capable of learning discriminative visual representations for solving vision tasks like classification, but that the learned representations are better for cross-modal retrieval when compared to supervised pre-training of the network on the ImageNet dataset.
	Address	Otawa; Canada; june 2019
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICMR
	Notes	DAG; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ PGR2019			Serial	3288
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	Author	V. Poulain d'Andecy; Emmanuel Hartmann; Marçal Rusiñol
	Title	Field Extraction by hybrid incremental and a-priori structural templates			Type	Conference Article
	Year	2018	Publication	13th IAPR International Workshop on Document Analysis Systems	Abbreviated Journal
	Volume		Issue		Pages	251 - 256
	Keywords	Layout Analysis; information extraction; incremental learning
	Abstract	In this paper, we present an incremental framework for extracting information fields from administrative documents. First, we demonstrate some limits of the existing state-of-the-art methods such as the delay of the system efficiency. This is a concern in industrial context when we have only few samples of each document class. Based on this analysis, we propose a hybrid system combining incremental learning by means of itf-df statistics and a-priori generic models. We report in the experimental section our results obtained with a dataset of real invoices.
	Address	Viena; Austria; April 2018
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	DAS
	Notes	DAG; 600.084; 600.129; 600.121			Approved	no
	Call Number	Admin @ si @ PHR2018			Serial	3106
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	Author	Lorenzo Porzi; Markus Hofinger; Idoia Ruiz; Joan Serrat; Samuel Rota Bulo; Peter Kontschieder
	Title	Learning Multi-Object Tracking and Segmentation from Automatic Annotations			Type	Conference Article
	Year	2020	Publication	33rd IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	6845-6854
	Keywords
	Abstract	In this work we contribute a novel pipeline to automatically generate training data, and to improve over state-of-the-art multi-object tracking and segmentation (MOTS) methods. Our proposed track mining algorithm turns raw street-level videos into high-fidelity MOTS training data, is scalable and overcomes the need of expensive and time-consuming manual annotation approaches. We leverage state-of-the-art instance segmentation results in combination with optical flow predictions, also trained on automatically harvested training data. Our second major contribution is MOTSNet – a deep learning, tracking-by-detection architecture for MOTS – deploying a novel mask-pooling layer for improved object association over time. Training MOTSNet with our automatically extracted data leads to significantly improved sMOTSA scores on the novel KITTI MOTS dataset (+1.9%/+7.5% on cars/pedestrians), and MOTSNet improves by +4.1% over previously best methods on the MOTSChallenge dataset. Our most impressive finding is that we can improve over previous best-performing works, even in complete absence of manually annotated MOTS training data.
	Address	virtual; June 2020
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPR
	Notes	ADAS; 600.124; 600.118			Approved	no
	Call Number	Admin @ si @ PHR2020			Serial	3402
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	Author	Monica Piñol
	Title	Adaptative Vocabulary Tree for Image Classification using Reinforcement Learning			Type	Report
	Year	2010	Publication	CVC Technical Report	Abbreviated Journal
	Volume	162	Issue		Pages
	Keywords
	Abstract
	Address	Bellaterra (Barcelona)
	Corporate Author	Computer Vision Center			Thesis	Master's thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ Piñ2010			Serial	1936
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	Author	Monica Piñol
	Title	Reinforcement Learning of Visual Descriptors for Object Recognition			Type	Book Whole
	Year	2014	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	The human visual system is able to recognize the object in an image even if the object is partially occluded, from various points of view, in different colors, or with independence of the distance to the object. To do this, the eye obtains an image and extracts features that are sent to the brain, and then, in the brain the object is recognized. In computer vision, the object recognition branch tries to learns from the human visual system behaviour to achieve its goal. Hence, an algorithm is used to identify representative features of the scene (detection), then another algorithm is used to describe these points (descriptor) and finally the extracted information is used for classifying the object in the scene. The selection of this set of algorithms is a very complicated task and thus, a very active research field. In this thesis we are focused on the selection/learning of the best descriptor for a given image. In the state of the art there are several descriptors but we do not know how to choose the best descriptor because depends on scenes that we will use (dataset) and the algorithm chosen to do the classification. We propose a framework based on reinforcement learning and bag of features to choose the best descriptor according to the given image. The system can analyse the behaviour of different learning algorithms and descriptor sets. Furthermore the proposed framework for improving the classification/recognition ratio can be used with minor changes in other computer vision fields, such as video retrieval.
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Ricardo Toledo;Angel Sappa
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-940902-5-7	Medium
	Area		Expedition		Conference
	Notes	ADAS; 600.076			Approved	no
	Call Number	Admin @ si @ Piñ2014			Serial	2464
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	Author	Xavier Perez Sala; Laura Igual; Sergio Escalera; Cecilio Angulo
	Title	Uniform Sampling of Rotations for Discrete and Continuous Learning of 2D Shape Models			Type	Book Chapter
	Year	2012	Publication	Vision Robotics: Technologies for Machine Learning and Vision Applications	Abbreviated Journal
	Volume		Issue	2	Pages	23-42
	Keywords
	Abstract	Different methodologies of uniform sampling over the rotation group, SO(3), for building unbiased 2D shape models from 3D objects are introduced and reviewed in this chapter. State-of-the-art non uniform sampling approaches are discussed, and uniform sampling methods using Euler angles and quaternions are introduced. Moreover, since presented work is oriented to model building applications, it is not limited to general discrete methods to obtain uniform 3D rotations, but also from a continuous point of view in the case of Procrustes Analysis.
	Address
	Corporate Author				Thesis
	Publisher	IGI-Global	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB;HuPBA			Approved	no
	Call Number	Admin @ si @ PIE2012			Serial	2064
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