| Records |
Author  |
A. Martinez; Jordi Vitria |
| Title |
Learning mixture models using a genetic version of the EM algorithm. |
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Journal Article |
| Year |
2000 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
21 |
Issue |
8 |
Pages |
759–769 |
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OR;MV |
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BCNPCL @ bcnpcl @ MVi2000 |
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335 |
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| Author |
A. Martinez; Jordi Vitria |
| Title |
A Development Plataform for Autonomous Agents. |
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Journal Article |
| Year |
1995 |
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ASI–AA–95 – Practice and Future of Autonomous Agents. |
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Monte Verita, Switzerland. |
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OR;MV |
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BCNPCL @ bcnpcl @ MaV1995b |
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123 |
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| Author |
A. Pujol; Jordi Vitria; Felipe Lumbreras; Juan J. Villanueva |
| Title |
Topological principal component analysis for face encoding and recognition |
Type |
Journal Article |
| Year |
2001 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
22 |
Issue |
6-7 |
Pages |
769–776 |
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IF: 0.552 |
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ADAS;OR;MV |
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no |
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ADAS @ adas @ PVL2001 |
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155 |
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| Author |
A. Sanfeliu; Juan J. Villanueva |
| Title |
An approach of visual motion analysis |
Type |
Journal Article |
| Year |
2005 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
26 |
Issue |
3 |
Pages |
355–368 |
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IF: 1.138 |
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ISE @ ise @ SaV2005 |
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561 |
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| Author |
A.F. Sole; Antonio Lopez; G. Sapiro |
| Title |
Crease Enhancement Diffusion |
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Journal Article |
| Year |
2001 |
Publication |
Computer Vision and Image Understanding, 84(2): 241–248 (IF: 1.298) |
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New York; USA |
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ADAS |
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no |
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ADAS @ adas @ SLS2001 |
Serial |
485 |
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| Author |
A.F. Sole; S. Ngan; G. Sapiro; X. Hu; Antonio Lopez |
| Title |
Anisotropic 2-D and 3-D Averaging of fMRI Signals |
Type |
Journal Article |
| Year |
2001 |
Publication |
IEEE Transactions on Medical Imaging |
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2020 |
Issue |
2 |
Pages |
86-93 |
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ADAS |
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no |
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ADAS @ adas @ SNS2001 |
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165 |
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| Author |
A.S. Coquel; Jean-Pascal Jacob; M. Primet; A. Demarez; Mariella Dimiccoli; T. Julou; L. Moisan; A. Lindner; H. Berry |
| Title |
Localization of protein aggregation in Escherichia coli is governed by diffusion and nucleoid macromolecular crowding effect |
Type |
Journal Article |
| Year |
2013 |
Publication |
Plos Computational Biology |
Abbreviated Journal |
PCB |
| Volume |
9 |
Issue |
4 |
Pages |
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| Abstract |
Aggregates of misfolded proteins are a hallmark of many age-related diseases. Recently, they have been linked to aging of Escherichia coli (E. coli) where protein aggregates accumulate at the old pole region of the aging bacterium. Because of the potential of E. coli as a model organism, elucidating aging and protein aggregation in this bacterium may pave the way to significant advances in our global understanding of aging. A first obstacle along this path is to decipher the mechanisms by which protein aggregates are targeted to specific intercellular locations. Here, using an integrated approach based on individual-based modeling, time-lapse fluorescence microscopy and automated image analysis, we show that the movement of aging-related protein aggregates in E. coli is purely diffusive (Brownian). Using single-particle tracking of protein aggregates in live E. coli cells, we estimated the average size and diffusion constant of the aggregates. Our results provide evidence that the aggregates passively diffuse within the cell, with diffusion constants that depend on their size in agreement with the Stokes-Einstein law. However, the aggregate displacements along the cell long axis are confined to a region that roughly corresponds to the nucleoid-free space in the cell pole, thus confirming the importance of increased macromolecular crowding in the nucleoids. We thus used 3D individual-based modeling to show that these three ingredients (diffusion, aggregation and diffusion hindrance in the nucleoids) are sufficient and necessary to reproduce the available experimental data on aggregate localization in the cells. Taken together, our results strongly support the hypothesis that the localization of aging-related protein aggregates in the poles of E. coli results from the coupling of passive diffusion-aggregation with spatially non-homogeneous macromolecular crowding. They further support the importance of “soft” intracellular structuring (based on macromolecular crowding) in diffusion-based protein localization in E. coli. |
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: Stanislav Shvartsman, Princeton University, United States of America |
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no |
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Admin @ si @CJP2013 |
Serial |
2786 |
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| Author |
Adriana Romero; Carlo Gatta; Gustavo Camps-Valls |
| Title |
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification |
Type |
Journal Article |
| Year |
2016 |
Publication |
IEEE Transaction on Geoscience and Remote Sensing |
Abbreviated Journal |
TGRS |
| Volume |
54 |
Issue |
3 |
Pages |
1349 - 1362 |
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| Abstract |
This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy. |
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0196-2892 |
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| Notes |
LAMP; 600.079;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ RGC2016 |
Serial |
2723 |
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| Author |
Adriana Romero; Petia Radeva; Carlo Gatta |
| Title |
Meta-parameter free unsupervised sparse feature learning |
Type |
Journal Article |
| Year |
2015 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
| Volume |
37 |
Issue |
8 |
Pages |
1716-1722 |
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We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on CIFAR-10, STL- 10 and UCMerced show that the method achieves the state-of-theart performance, providing discriminative features that generalize well. |
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MILAB; 600.068; 600.079; 601.160 |
Approved |
no |
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Admin @ si @ RRG2014b |
Serial |
2594 |
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| Author |
Adrien Gaidon; Antonio Lopez; Florent Perronnin |
| Title |
The Reasonable Effectiveness of Synthetic Visual Data |
Type |
Journal Article |
| Year |
2018 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
| Volume |
126 |
Issue |
9 |
Pages |
899–901 |
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ADAS; 600.118 |
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no |
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Admin @ si @ GLP2018 |
Serial |
3180 |
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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 |
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Journal Article |
| Year |
2023 |
Publication |
Journal of Machine Learning Research |
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JMLR |
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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. |
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HUPBA |
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no |
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Admin @ si @ PGL2023 |
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3973 |
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| Author |
Aitor Alvarez-Gila; Adrian Galdran; Estibaliz Garrote; Joost Van de Weijer |
| Title |
Self-supervised blur detection from synthetically blurred scenes |
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Journal Article |
| Year |
2019 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
IMAVIS |
| Volume |
92 |
Issue |
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Pages |
103804 |
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Blur detection aims at segmenting the blurred areas of a given image. Recent deep learning-based methods approach this problem by learning an end-to-end mapping between the blurred input and a binary mask representing the localization of its blurred areas. Nevertheless, the effectiveness of such deep models is limited due to the scarcity of datasets annotated in terms of blur segmentation, as blur annotation is labor intensive. In this work, we bypass the need for such annotated datasets for end-to-end learning, and instead rely on object proposals and a model for blur generation in order to produce a dataset of synthetically blurred images. This allows us to perform self-supervised learning over the generated image and ground truth blur mask pairs using CNNs, defining a framework that can be employed in purely self-supervised, weakly supervised or semi-supervised configurations. Interestingly, experimental results of such setups over the largest blur segmentation datasets available show that this approach achieves state of the art results in blur segmentation, even without ever observing any real blurred image. |
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LAMP; 600.109; 600.120 |
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no |
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Admin @ si @ AGG2019 |
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3301 |
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Ajian Liu; Chenxu Zhao; Zitong Yu; Jun Wan; Anyang Su; Xing Liu; Zichang Tan; Sergio Escalera; Junliang Xing; Yanyan Liang; Guodong Guo; Zhen Lei; Stan Z. Li; Shenshen Du |
| Title |
Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection |
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Journal Article |
| Year |
2022 |
Publication |
IEEE Transactions on Information Forensics and Security |
Abbreviated Journal |
TIForensicSEC |
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17 |
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2497 - 2507 |
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Face presentation attack detection (PAD) is essential to secure face recognition systems primarily from high-fidelity mask attacks. Most existing 3D mask PAD benchmarks suffer from several drawbacks: 1) a limited number of mask identities, types of sensors, and a total number of videos; 2) low-fidelity quality of facial masks. Basic deep models and remote photoplethysmography (rPPG) methods achieved acceptable performance on these benchmarks but still far from the needs of practical scenarios. To bridge the gap to real-world applications, we introduce a large-scale Hi gh- Fi delity Mask dataset, namely HiFiMask . Specifically, a total amount of 54,600 videos are recorded from 75 subjects with 225 realistic masks by 7 new kinds of sensors. Along with the dataset, we propose a novel C ontrastive C ontext-aware L earning (CCL) framework. CCL is a new training methodology for supervised PAD tasks, which is able to learn by leveraging rich contexts accurately (e.g., subjects, mask material and lighting) among pairs of live faces and high-fidelity mask attacks. Extensive experimental evaluations on HiFiMask and three additional 3D mask datasets demonstrate the effectiveness of our method. The codes and dataset will be released soon. |
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IEEE |
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HuPBA |
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no |
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Admin @ si @ LZY2022 |
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3778 |
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Ajian Liu; Xuan Li; Jun Wan; Yanyan Liang; Sergio Escalera; Hugo Jair Escalante; Meysam Madadi; Yi Jin; Zhuoyuan Wu; Xiaogang Yu; Zichang Tan; Qi Yuan; Ruikun Yang; Benjia Zhou; Guodong Guo; Stan Z. Li |
| Title |
Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review |
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Journal Article |
| Year |
2020 |
Publication |
IET Biometrics |
Abbreviated Journal |
BIO |
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10 |
Issue |
1 |
Pages |
24-43 |
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| Abstract |
Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has %possessed achieved impressive progress recently due the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing. Recently, a multi-ethnic face anti-spoofing dataset, CASIA-SURF CeFA, has been released with the goal of measuring the ethnic bias. It is the largest up to date cross-ethnicity face anti-spoofing dataset covering 3 ethnicities, 3 modalities, 1,607 subjects, 2D plus 3D attack types, and the first dataset including explicit ethnic labels among the recently released datasets for face anti-spoofing. We organized the Chalearn Face Anti-spoofing Attack Detection Challenge which consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have 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. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions. |
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HUPBA; no proj |
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no |
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Admin @ si @ LLW2020b |
Serial |
3523 |
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| Author |
Akhil Gurram; Ahmet Faruk Tuna; Fengyi Shen; Onay Urfalioglu; Antonio Lopez |
| Title |
Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision |
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Journal Article |
| Year |
2021 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
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23 |
Issue |
8 |
Pages |
12738-12751 |
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Depth information is essential for on-board perception in autonomous driving and driver assistance. Monocular depth estimation (MDE) is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground truth (GT). Usually, this GT is acquired at training time through a calibrated multi-modal suite of sensors. However, also using only a monocular system at training time is cheaper and more scalable. This is possible by relying on structure-from-motion (SfM) principles to generate self-supervision. Nevertheless, problems of camouflaged objects, visibility changes, static-camera intervals, textureless areas, and scale ambiguity, diminish the usefulness of such self-supervision. In this paper, we perform monocular depth estimation by virtual-world supervision (MonoDEVS) and real-world SfM self-supervision. We compensate the SfM self-supervision limitations by leveraging virtual-world images with accurate semantic and depth supervision and addressing the virtual-to-real domain gap. Our MonoDEVSNet outperforms previous MDE CNNs trained on monocular and even stereo sequences. |
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ADAS; 600.118 |
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no |
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Admin @ si @ GTS2021 |
Serial |
3598 |
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