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Jiaolong Xu; Liang Xiao; Antonio Lopez |
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Title |
Self-supervised Domain Adaptation for Computer Vision Tasks |
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Journal Article |
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2019 |
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IEEE Access |
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ACCESS |
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7 |
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156694 - 156706 |
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Recent progress of self-supervised visual representation learning has achieved remarkable success on many challenging computer vision benchmarks. However, whether these techniques can be used for domain adaptation has not been explored. In this work, we propose a generic method for self-supervised domain adaptation, using object recognition and semantic segmentation of urban scenes as use cases. Focusing on simple pretext/auxiliary tasks (e.g. image rotation prediction), we assess different learning strategies to improve domain adaptation effectiveness by self-supervision. Additionally, we propose two complementary strategies to further boost the domain adaptation accuracy on semantic segmentation within our method, consisting of prediction layer alignment and batch normalization calibration. The experimental results show adaptation levels comparable to most studied domain adaptation methods, thus, bringing self-supervision as a new alternative for reaching domain adaptation. The code is available at this link. https://github.com/Jiaolong/self-supervised-da. |
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ADAS; 600.118 |
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Admin @ si @ XXL2019 |
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3302 |
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Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa |
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Title |
Learning a Part-based Pedestrian Detector in Virtual World |
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Journal Article |
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2014 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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15 |
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5 |
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2121-2131 |
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Domain Adaptation; Pedestrian Detection; Virtual Worlds |
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Detecting pedestrians with on-board vision systems is of paramount interest for assisting drivers to prevent vehicle-to-pedestrian accidents. The core of a pedestrian detector is its classification module, which aims at deciding if a given image window contains a pedestrian. Given the difficulty of this task, many classifiers have been proposed during the last fifteen years. Among them, the so-called (deformable) part-based classifiers including multi-view modeling are usually top ranked in accuracy. Training such classifiers is not trivial since a proper aspect clustering and spatial part alignment of the pedestrian training samples are crucial for obtaining an accurate classifier. In this paper, first we perform automatic aspect clustering and part alignment by using virtual-world pedestrians, i.e., human annotations are not required. Second, we use a mixture-of-parts approach that allows part sharing among different aspects. Third, these proposals are integrated in a learning framework which also allows to incorporate real-world training data to perform domain adaptation between virtual- and real-world cameras. Overall, the obtained results on four popular on-board datasets show that our proposal clearly outperforms the state-of-the-art deformable part-based detector known as latent SVM. |
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1931-0587 |
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978-1-4673-2754-1 |
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ADAS; 600.076 |
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ADAS @ adas @ XVL2014 |
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2433 |
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Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Ludmila I. Kuncheva |
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Title |
Occlusion handling via random subspace classifiers for human detection |
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2014 |
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IEEE Transactions on Systems, Man, and Cybernetics (Part B) |
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TSMCB |
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44 |
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3 |
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342-354 |
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Pedestriand Detection; occlusion handling |
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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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2168-2267 |
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ADAS; 605.203; 600.057; 600.054; 601.042; 601.187; 600.076 |
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ADAS @ adas @ MVL2014 |
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2213 |
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Jaume Amores; Petia Radeva |
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Registration and Retrieval of Highly Elastic Bodies using Contextual Information |
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2005 |
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Pattern Recognition Letters |
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PRL |
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26 |
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11 |
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1720–1731 |
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IF: 1.138 |
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ADAS @ adas @ AmR2005b |
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592 |
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Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Jaume Amores; Petia Radeva |
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Title |
Retrieval of IVUS Images Using Contextual Information and Elastic Matching |
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2005 |
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International Journal on Intelligent Systems, 20(5):541–560 (IF: 0.657) |
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ADAS @ adas @ AmR2005a |
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