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Author ![]() |
Javad Zolfaghari Bengar; Joost Van de Weijer; Laura Lopez-Fuentes; Bogdan Raducanu | ||||
Title | Class-Balanced Active Learning for Image Classification | Type | Conference Article | ||
Year | 2022 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Active learning aims to reduce the labeling effort that is required to train algorithms by learning an acquisition function selecting the most relevant data for which a label should be requested from a large unlabeled data pool. Active learning is generally studied on balanced datasets where an equal amount of images per class is available. However, real-world datasets suffer from severe imbalanced classes, the so called long-tail distribution. We argue that this further complicates the active learning process, since the imbalanced data pool can result in suboptimal classifiers. To address this problem in the context of active learning, we proposed a general optimization framework that explicitly takes class-balancing into account. Results on three datasets showed that the method is general (it can be combined with most existing active learning algorithms) and can be effectively applied to boost the performance of both informative and representative-based active learning methods. In addition, we showed that also on balanced datasets
our method 1 generally results in a performance gain. |
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Address | Virtual; Waikoloa; Hawai; USA; January 2022 | ||||
Corporate Author | Thesis | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | WACV | ||
Notes | LAMP; 602.200; 600.147; 600.120 | Approved | no | ||
Call Number | Admin @ si @ ZWL2022 | Serial | 3703 | ||
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Author ![]() |
Javier Jimenez; Antonio Lopez; Joan Serrat | ||||
Title | Un enfoque ABP aplicado a Ingenieria del Software | Type | Miscellaneous | ||
Year | 2007 | Publication | Seminario Internacional RED–U 2–07 para El desarrollo de la autonomia en el aprendizaje | Abbreviated Journal | |
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Address | Barcelona (Spain) | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ JLS2007 | Serial | 937 | ||
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Author ![]() |
Javier M. Olaso; Alain Vazquez; Leila Ben Letaifa; Mikel de Velasco; Aymen Mtibaa; Mohamed Amine Hmani; Dijana Petrovska-Delacretaz; Gerard Chollet; Cesar Montenegro; Asier Lopez-Zorrilla; Raquel Justo; Roberto Santana; Jofre Tenorio-Laranga; Eduardo Gonzalez-Fraile; Begoña Fernandez-Ruanova; Gennaro Cordasco; Anna Esposito; Kristin Beck Gjellesvik; Anna Torp Johansen; Maria Stylianou Kornes; Colin Pickard; Cornelius Glackin; Gary Cahalane; Pau Buch; Cristina Palmero; Sergio Escalera; Olga Gordeeva; Olivier Deroo; Anaïs Fernandez; Daria Kyslitska; Jose Antonio Lozano; Maria Ines Torres; Stephan Schlogl | ||||
Title | The EMPATHIC Virtual Coach: a demo | Type | Conference Article | ||
Year | 2021 | Publication | 23rd ACM International Conference on Multimodal Interaction | Abbreviated Journal | |
Volume | Issue | Pages | 848-851 | ||
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Abstract | The main objective of the EMPATHIC project has been the design and development of a virtual coach to engage the healthy-senior user and to enhance well-being through awareness of personal status. The EMPATHIC approach addresses this objective through multimodal interactions supported by the GROW coaching model. The paper summarizes the main components of the EMPATHIC Virtual Coach (EMPATHIC-VC) and introduces a demonstration of the coaching sessions in selected scenarios. | ||||
Address | Virtual; October 2021 | ||||
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 | ICMI | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ OVB2021 | Serial | 3644 | ||
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Author ![]() |
Javier Marin | ||||
Title | Pedestrian Detection Based on Local Experts | Type | Book Whole | ||
Year | 2013 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | During the last decade vision-based human detection systems have started to play a key rolein multiple applications linked to driver assistance, surveillance, robot sensing and home automation.
Detecting humans is by far one of the most challenging tasks in Computer Vision. This is mainly due to the high degree of variability in the human appearanceassociated to the clothing, pose, shape and size. Besides, other factors such as cluttered scenarios, partial occlusions, or environmental conditions can make the detection task even harder. Most promising methods of the state-of-the-art rely on discriminative learning paradigms which are fed with positive and negative examples. The training data is one of the most relevant elements in order to build a robust detector as it has to cope the large variability of the target. In order to create this dataset human supervision is required. The drawback at this point is the arduous effort of annotating as well as looking for such claimed variability. In this PhD thesis we address two recurrent problems in the literature. In the first stage,we aim to reduce the consuming task of annotating, namely, by using computer graphics. More concretely, we develop a virtual urban scenario for later generating a pedestrian dataset. Then, we train a detector using this dataset, and finally we assess if this detector can be successfully applied in a real scenario. In the second stage, we focus on increasing the robustness of our pedestrian detectors under partial occlusions. In particular, we present a novel occlusion handling approach to increase the performance of block-based holistic methods under partial occlusions. For this purpose, we make use of local experts via a RandomSubspaceMethod (RSM) to handle these cases. If the method infers a possible partial occlusion, then the RSM, based on performance statistics obtained from partially occluded data, is applied. The last objective of this thesis is to propose a robust pedestrian detector based on an ensemble of local experts. To achieve this goal, we use the random forest paradigm, where the trees act as ensembles an their nodesare the local experts. In particular, each expert focus on performing a robust classification ofa pedestrian body patch. This approach offers computational efficiency and far less design complexity when compared to other state-of-the-artmethods, while reaching better accuracy |
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Address | Barcelona | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Antonio Lopez;Jaume Amores | |
Language | Summary Language | Original Title | |||
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Area | Expedition | Conference | |||
Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ Mar2013 | Serial | 2280 | ||
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Author ![]() |
Javier Marin | ||||
Title | Virtual learning for real testing | Type | Report | ||
Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal | |
Volume | 150 | Issue | Pages | ||
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Corporate Author | Computer Vision Center | Thesis | Master's thesis | ||
Publisher | Place of Publication | bell | 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 @ Mar2009c | Serial | 2403 | ||
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Author ![]() |
Javier Marin; David Geronimo; David Vazquez; Antonio Lopez | ||||
Title | Pedestrian Detection: Exploring Virtual Worlds | Type | Book Chapter | ||
Year | 2012 | Publication | Handbook of Pattern Recognition: Methods and Application | Abbreviated Journal | |
Volume | 5 | Issue | Pages | 145-162 | |
Keywords | Virtual worlds; Pedestrian Detection; Domain Adaptation | ||||
Abstract | Handbook of pattern recognition will include contributions from university educators and active research experts. This Handbook is intended to serve as a basic reference on methods and applications of pattern recognition. The primary aim of this handbook is providing the community of pattern recognition with a readable, easy to understand resource that covers introductory, intermediate and advanced topics with equal clarity. Therefore, the Handbook of pattern recognition can serve equally well as reference resource and as classroom textbook. Contributions cover all methods, techniques and applications of pattern recognition. A tentative list of relevant topics might include: 1- Statistical, structural, syntactic pattern recognition. 2- Neural networks, machine learning, data mining. 3- Discrete geometry, algebraic, graph-based techniques for pattern recognition. 4- Face recognition, Signal analysis, image coding and processing, shape and texture analysis. 5- Document processing, text and graphics recognition, digital libraries. 6- Speech recognition, music analysis, multimedia systems. 7- Natural language analysis, information retrieval. 8- Biometrics, biomedical pattern analysis and information systems. 9- Other scientific, engineering, social and economical applications of pattern recognition. 10- Special hardware architectures, software packages for pattern recognition. | ||||
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Publisher | iConcept Press | Place of Publication | Editor | ||
Language | English | Summary Language | Original Title | ||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-1-477554-82-1 | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ MGV2012 | Serial | 1979 | ||
Permanent link to this record | |||||
Author ![]() |
Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Bastian Leibe | ||||
Title | Random Forests of Local Experts for Pedestrian Detection | Type | Conference Article | ||
Year | 2013 | Publication | 15th IEEE International Conference on Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 2592 - 2599 | ||
Keywords | ADAS; Random Forest; Pedestrian Detection | ||||
Abstract | Pedestrian detection is one of the most challenging tasks in computer vision, and has received a lot of attention in the last years. Recently, some authors have shown the advantages of using combinations of part/patch-based detectors in order to cope with the large variability of poses and the existence of partial occlusions. In this paper, we propose a pedestrian detection method that efficiently combines multiple local experts by means of a Random Forest ensemble. The proposed method works with rich block-based representations such as HOG and LBP, in such a way that the same features are reused by the multiple local experts, so that no extra computational cost is needed with respect to a holistic method. Furthermore, we demonstrate how to integrate the proposed approach with a cascaded architecture in order to achieve not only high accuracy but also an acceptable efficiency. In particular, the resulting detector operates at five frames per second using a laptop machine. We tested the proposed method with well-known challenging datasets such as Caltech, ETH, Daimler, and INRIA. The method proposed in this work consistently ranks among the top performers in all the datasets, being either the best method or having a small difference with the best one. | ||||
Address | Sydney; Australia; December 2013 | ||||
Corporate Author | Thesis | ||||
Publisher | IEEE | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | 1550-5499 | ISBN | Medium | ||
Area | Expedition | Conference | ICCV | ||
Notes | ADAS; 600.057; 600.054 | Approved | no | ||
Call Number | ADAS @ adas @ MVL2013 | Serial | 2333 | ||
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Author ![]() |
Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Ludmila I. Kuncheva | ||||
Title | Occlusion handling via random subspace classifiers for human detection | Type | Journal Article | ||
Year | 2014 | Publication | IEEE Transactions on Systems, Man, and Cybernetics (Part B) | Abbreviated Journal | TSMCB |
Volume | 44 | Issue | 3 | Pages | 342-354 |
Keywords | Pedestriand Detection; occlusion handling | ||||
Abstract | This paper describes a general method to address partial occlusions for human detection in still images. The Random Subspace Method (RSM) is chosen for building a classifier ensemble robust against partial occlusions. The component classifiers are chosen on the basis of their individual and combined performance. The main contribution of this work lies in our approach’s capability to improve the detection rate when partial occlusions are present without compromising the detection performance on non occluded data. In contrast to many recent approaches, we propose a method which does not require manual labelling of body parts, defining any semantic spatial components, or using additional data coming from motion or stereo. Moreover, the method can be easily extended to other object classes. The experiments are performed on three large datasets: the INRIA person dataset, the Daimler Multicue dataset, and a new challenging dataset, called PobleSec, in which a considerable number of targets are partially occluded. The different approaches are evaluated at the classification and detection levels for both partially occluded and non-occluded data. The experimental results show that our detector outperforms state-of-the-art approaches in the presence of partial occlusions, while offering performance and reliability similar to those of the holistic approach on non-occluded data. The datasets used in our experiments have been made publicly available for benchmarking purposes | ||||
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Series Volume | Series Issue | Edition | |||
ISSN | 2168-2267 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 605.203; 600.057; 600.054; 601.042; 601.187; 600.076 | Approved | no | ||
Call Number | ADAS @ adas @ MVL2014 | Serial | 2213 | ||
Permanent link to this record | |||||
Author ![]() |
Javier Marin; David Vazquez; David Geronimo; Antonio Lopez | ||||
Title | Learning Appearance in Virtual Scenarios for Pedestrian Detection | Type | Conference Article | ||
Year | 2010 | Publication | 23rd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 137–144 | ||
Keywords | Pedestrian Detection; Domain Adaptation | ||||
Abstract | Detecting pedestrians in images is a key functionality to avoid vehicle-to-pedestrian collisions. The most promising detectors rely on appearance-based pedestrian classifiers trained with labelled samples. This paper addresses the following question: can a pedestrian appearance model learnt in virtual scenarios work successfully for pedestrian detection in real images? (Fig. 1). Our experiments suggest a positive answer, which is a new and relevant conclusion for research in pedestrian detection. More specifically, we record training sequences in virtual scenarios and then appearance-based pedestrian classifiers are learnt using HOG and linear SVM. We test such classifiers in a publicly available dataset provided by Daimler AG for pedestrian detection benchmarking. This dataset contains real world images acquired from a moving car. The obtained result is compared with the one given by a classifier learnt using samples coming from real images. The comparison reveals that, although virtual samples were not specially selected, both virtual and real based training give rise to classifiers of similar performance. | ||||
Address | San Francisco; CA; USA; June 2010 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | English | Summary Language | English | Original Title | Learning Appearance in Virtual Scenarios for Pedestrian Detection |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1063-6919 | ISBN | 978-1-4244-6984-0 | Medium | |
Area | Expedition | Conference | CVPR | ||
Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ MVG2010 | Serial | 1304 | ||
Permanent link to this record | |||||
Author ![]() |
Javier Marin; Sergio Escalera | ||||
Title | SSSGAN: Satellite Style and Structure Generative Adversarial Networks | Type | Journal Article | ||
Year | 2021 | Publication | Remote Sensing | Abbreviated Journal | |
Volume | 13 | Issue | 19 | Pages | 3984 |
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Abstract | This work presents Satellite Style and Structure Generative Adversarial Network (SSGAN), a generative model of high resolution satellite imagery to support image segmentation. Based on spatially adaptive denormalization modules (SPADE) that modulate the activations with respect to segmentation map structure, in addition to global descriptor vectors that capture the semantic information in a vector with respect to Open Street Maps (OSM) classes, this model is able to produce
consistent aerial imagery. By decoupling the generation of aerial images into a structure map and a carefully defined style vector, we were able to improve the realism and geodiversity of the synthesis with respect to the state-of-the-art baseline. Therefore, the proposed model allows us to control the generation not only with respect to the desired structure, but also with respect to a geographic area. |
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ MaE2021 | Serial | 3651 | ||
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Author ![]() |
Javier Rodenas; Bhalaji Nagarajan; Marc Bolaños; Petia Radeva | ||||
Title | Learning Multi-Subset of Classes for Fine-Grained Food Recognition | Type | Conference Article | ||
Year | 2022 | Publication | 7th International Workshop on Multimedia Assisted Dietary Management | Abbreviated Journal | |
Volume | Issue | Pages | 17–26 | ||
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Abstract | Food image recognition is a complex computer vision task, because of the large number of fine-grained food classes. Fine-grained recognition tasks focus on learning subtle discriminative details to distinguish similar classes. In this paper, we introduce a new method to improve the classification of classes that are more difficult to discriminate based on Multi-Subsets learning. Using a pre-trained network, we organize classes in multiple subsets using a clustering technique. Later, we embed these subsets in a multi-head model structure. This structure has three distinguishable parts. First, we use several shared blocks to learn the generalized representation of the data. Second, we use multiple specialized blocks focusing on specific subsets that are difficult to distinguish. Lastly, we use a fully connected layer to weight the different subsets in an end-to-end manner by combining the neuron outputs. We validated our proposed method using two recent state-of-the-art vision transformers on three public food recognition datasets. Our method was successful in learning the confused classes better and we outperformed the state-of-the-art on the three datasets. | ||||
Address | Lisboa; Portugal; October 2022 | ||||
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Area | Expedition | Conference | MADiMa | ||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ RNB2022 | Serial | 3797 | ||
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Author ![]() |
Javier Selva; Anders S. Johansen; Sergio Escalera; Kamal Nasrollahi; Thomas B. Moeslund; Albert Clapes | ||||
Title | Video transformers: A survey | Type | Journal Article | ||
Year | 2023 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 45 | Issue | 11 | Pages | 12922-12943 |
Keywords | Artificial Intelligence; Computer Vision; Self-Attention; Transformers; Video Representations | ||||
Abstract | Transformer models have shown great success handling long-range interactions, making them a promising tool for modeling video. However, they lack inductive biases and scale quadratically with input length. These limitations are further exacerbated when dealing with the high dimensionality introduced by the temporal dimension. While there are surveys analyzing the advances of Transformers for vision, none focus on an in-depth analysis of video-specific designs. In this survey, we analyze the main contributions and trends of works leveraging Transformers to model video. Specifically, we delve into how videos are handled at the input level first. Then, we study the architectural changes made to deal with video more efficiently, reduce redundancy, re-introduce useful inductive biases, and capture long-term temporal dynamics. In addition, we provide an overview of different training regimes and explore effective self-supervised learning strategies for video. Finally, we conduct a performance comparison on the most common benchmark for Video Transformers (i.e., action classification), finding them to outperform 3D ConvNets even with less computational complexity. | ||||
Address | 1 Nov. 2023 | ||||
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Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ SJE2023 | Serial | 3823 | ||
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Author ![]() |
Javier Varona | ||||
Title | Seguimiento visual robusto en entornos complejos, Tesis. | Type | Miscellaneous | ||
Year | 2001 | Publication | Abbreviated Journal | ||
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Notes | Approved | no | |||
Call Number | Admin @ si @ Var2001 | Serial | 214 | ||
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Author ![]() |
Javier Varona; A. Pujol; Juan J. Villanueva | ||||
Title | Visual tracking in application domains. | Type | Miscellaneous | ||
Year | 1999 | Publication | Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. | Abbreviated Journal | |
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Address | Bilbao | ||||
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Notes | Approved | no | |||
Call Number | ISE @ ise @ VPV1999 | Serial | 10 | ||
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Author ![]() |
Javier Varona; A. Pujol; Juan J. Villanueva | ||||
Title | Visual Tracking in Application Domains. | Type | Miscellaneous | ||
Year | 2000 | Publication | Pattern Recognition and Applications, IOS Press, 99–106. | Abbreviated Journal | |
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Notes | Approved | no | |||
Call Number | ISE @ ise @ VPV2000 | Serial | 333 | ||
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