| |
Records |
Links |
|
Author |
David Vazquez; Antonio Lopez; Daniel Ponsa |
|
| |
Title |
Unsupervised Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection |
Type |
Conference Article |
| |
Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
3492 - 3495 |
|
| |
Keywords |
Pedestrian Detection; Domain Adaptation; Virtual worlds |
|
| |
Abstract |
Vision-based object detectors are crucial for different applications. They rely on learnt object models. Ideally, we would like to deploy our vision system in the scenario where it must operate, and lead it to self-learn how to distinguish the objects of interest, i.e., without human intervention. However, the learning of each object model requires labelled samples collected through a tiresome manual process. For instance, we are interested in exploring the self-training of a pedestrian detector for driver assistance systems. Our first approach to avoid manual labelling consisted in the use of samples coming from realistic computer graphics, so that their labels are automatically available [12]. This would make possible the desired self-training of our pedestrian detector. However, as we showed in [14], between virtual and real worlds it may be a dataset shift. In order to overcome it, we propose the use of unsupervised domain adaptation techniques that avoid human intervention during the adaptation process. In particular, this paper explores the use of the transductive SVM (T-SVM) learning algorithm in order to adapt virtual and real worlds for pedestrian detection (Fig. 1). |
|
| |
Address |
Tsukuba Science City, Japan |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
IEEE |
Place of Publication |
Tsukuba Science City, JAPAN |
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference  |
ICPR |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ VLP2012 |
Serial |
1981 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez; N. Paragios |
|
| |
Title |
Image Contextual Representation and Matching through Hierarchies and Higher Order Graphs |
Type |
Conference Article |
| |
Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
2664 - 2667 |
|
| |
Keywords |
|
|
| |
Abstract |
We present a region matching algorithm which establishes correspondences between regions from two segmented images. An abstract graph-based representation conceals the image in a hierarchical graph, exploiting the scene properties at two levels. First, the similarity and spatial consistency of the image semantic objects is encoded in a graph of commute times. Second, the cluttered regions of the semantic objects are represented with a shape descriptor. Many-to-many matching of regions is specially challenging due to the instability of the segmentation under slight image changes, and we explicitly handle it through high order potentials. We demonstrate the matching approach applied to images of world famous buildings, captured under different conditions, showing the robustness of our method to large variations in illumination and viewpoint. |
|
| |
Address |
Tsukuba Science City, Japan |
|
| |
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 |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICPR |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RSL2012a; |
Serial |
2032 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
German Ros; Jesus Martinez del Rincon; Gines Garcia-Mateos |
|
| |
Title |
Articulated Particle Filter for Hand Tracking |
Type |
Conference Article |
| |
Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
3581 - 3585 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper proposes a new version of Particle Filter, called Articulated Particle Filter – ArPF -, which has been specifically designed for an efficient sampling of hierarchical spaces, generated by articulated objects. Our approach decomposes the articulated motion into layers for efficiency purposes, making use of a careful modeling of the diffusion noise along with its propagation through the articulations. This produces an increase of accuracy and prevent for divergences. The algorithm is tested on hand tracking due to its complex hierarchical articulated nature. With this purpose, a new dataset generation tool for quantitative evaluation is also presented in this paper. |
|
| |
Address |
Tsukuba Science City, Japan |
|
| |
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 |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICPR |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RMG2012 |
Serial |
2031 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jiaolong Xu; Sebastian Ramos;David Vazquez; Antonio Lopez |
|
| |
Title |
Cost-sensitive Structured SVM for Multi-category Domain Adaptation |
Type |
Conference Article |
| |
Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
3886 - 3891 |
|
| |
Keywords |
Domain Adaptation; Pedestrian Detection |
|
| |
Abstract |
Domain adaptation addresses the problem of accuracy drop that a classifier may suffer when the training data (source domain) and the testing data (target domain) are drawn from different distributions. In this work, we focus on domain adaptation for structured SVM (SSVM). We propose a cost-sensitive domain adaptation method for SSVM, namely COSS-SSVM. In particular, during the re-training of an adapted classifier based on target and source data, the idea that we explore consists in introducing a non-zero cost even for correctly classified source domain samples. Eventually, we aim to learn a more targetoriented classifier by not rewarding (zero loss) properly classified source-domain training samples. We assess the effectiveness of COSS-SSVM on multi-category object recognition. |
|
| |
Address |
Stockholm; Sweden; August 2014 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
IEEE |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1051-4651 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICPR |
|
| |
Notes |
ADAS; 600.057; 600.054; 601.217; 600.076 |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ XRV2014a |
Serial |
2434 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Xialei Liu; Marc Masana; Luis Herranz; Joost Van de Weijer; Antonio Lopez; Andrew Bagdanov |
|
| |
Title |
Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting |
Type |
Conference Article |
| |
Year |
2018 |
Publication |
24th International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
2262-2268 |
|
| |
Keywords |
|
|
| |
Abstract |
In this paper we propose an approach to avoiding catastrophic forgetting in sequential task learning scenarios. Our technique is based on a network reparameterization that approximately diagonalizes the Fisher Information Matrix of the network parameters. This reparameterization takes the form of
a factorized rotation of parameter space which, when used in conjunction with Elastic Weight Consolidation (which assumes a diagonal Fisher Information Matrix), leads to significantly better performance on lifelong learning of sequential tasks. Experimental results on the MNIST, CIFAR-100, CUB-200 and
Stanford-40 datasets demonstrate that we significantly improve the results of standard elastic weight consolidation, and that we obtain competitive results when compared to the state-of-the-art in lifelong learning without forgetting. |
|
| |
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 |
ICPR |
|
| |
Notes |
LAMP; ADAS; 601.305; 601.109; 600.124; 600.106; 602.200; 600.120; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ LMH2018 |
Serial |
3160 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Idoia Ruiz; Joan Serrat |
|
| |
Title |
Rank-based ordinal classification |
Type |
Conference Article |
| |
Year |
2020 |
Publication |
25th International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
8069-8076 |
|
| |
Keywords |
|
|
| |
Abstract |
Differently from the regular classification task, in ordinal classification there is an order in the classes. As a consequence not all classification errors matter the same: a predicted class close to the groundtruth one is better than predicting a farther away class. To account for this, most previous works employ loss functions based on the absolute difference between the predicted and groundtruth class labels. We argue that there are many cases in ordinal classification where label values are arbitrary (for instance 1. . . C, being C the number of classes) and thus such loss functions may not be the best choice. We instead propose a network architecture that produces not a single class prediction but an ordered vector, or ranking, of all the possible classes from most to least likely. This is thanks to a loss function that compares groundtruth and predicted rankings of these class labels, not the labels themselves. Another advantage of this new formulation is that we can enforce consistency in the predictions, namely, predicted rankings come from some unimodal vector of scores with mode at the groundtruth class. We compare with the state of the art ordinal classification methods, showing
that ours attains equal or better performance, as measured by common ordinal classification metrics, on three benchmark datasets. Furthermore, it is also suitable for a new task on image aesthetics assessment, i.e. most voted score prediction. Finally, we also apply it to building damage assessment from satellite images, providing an analysis of its performance depending on the degree of imbalance of the dataset. |
|
| |
Address |
Virtual; January 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 |
ICPR |
|
| |
Notes |
ADAS; 600.118; 600.124 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RuS2020 |
Serial |
3549 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Diego Cheda; Daniel Ponsa; Antonio Lopez |
|
| |
Title |
Monocular Egomotion Estimation based on Image Matching |
Type |
Conference Article |
| |
Year |
2012 |
Publication |
1st International Conference on Pattern Recognition Applications and Methods |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
425-430 |
|
| |
Keywords |
SLAM |
|
| |
Abstract |
|
|
| |
Address |
Portugal |
|
| |
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 |
ICPRAM |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
Admin @ si @ CPL2012a;; ADAS @ adas @ |
Serial |
2011 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez |
|
| |
Title |
Multiple target tracking and identity linking under split, merge and occlusion of targets and observations |
Type |
Conference Article |
| |
Year |
2012 |
Publication |
1st International Conference on Pattern Recognition Applications and Methods |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
Algarve, Portugal |
|
| |
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 |
ICPRAM |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RSL2012c; ADAS @ adas |
Serial |
2034 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Ferran Diego; G.D. Evangelidis; Joan Serrat |
|
| |
Title |
Night-time outdoor surveillance by mobile cameras |
Type |
Conference Article |
| |
Year |
2012 |
Publication |
1st International Conference on Pattern Recognition Applications and Methods |
Abbreviated Journal |
|
|
| |
Volume |
2 |
Issue |
|
Pages |
365-371 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper addresses the problem of video surveillance by mobile cameras. We present a method that allows online change detection in night-time outdoor surveillance. Because of the camera movement, background frames are not available and must be “localized” in former sequences and registered with the current frames. To this end, we propose a Frame Localization And Registration (FLAR) approach that solves the problem efficiently. Frames of former sequences define a database which is queried by current frames in turn. To quickly retrieve nearest neighbors, database is indexed through a visual dictionary method based on the SURF descriptor. Furthermore, the frame localization is benefited by a temporal filter that exploits the temporal coherence of videos. Next, the recently proposed ECC alignment scheme is used to spatially register the synchronized frames. Finally, change detection methods apply to aligned frames in order to mark suspicious areas. Experiments with real night sequences recorded by in-vehicle cameras demonstrate the performance of the proposed method and verify its efficiency and effectiveness against other methods. |
|
| |
Address |
Algarve, Portugal |
|
| |
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 |
ICPRAM |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
Admin @ si @ DES2012 |
Serial |
2035 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Hugo Berti; Angel Sappa; Osvaldo Agamennoni |
|
| |
Title |
Autonomous robot navigation with a global and asymptotic convergence |
Type |
Conference Article |
| |
Year |
2007 |
Publication |
IEEE International Conference on Robotics and Automation |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
2712–2717 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
Roma (Italy) |
|
| |
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 |
ICRA |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ BSA2007 |
Serial |
796 |
|
| Permanent link to this record |
