| |
Records |
Links |
|
Author |
Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez |
|
| |
Title |
End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth data |
Type |
Journal Article |
| |
Year |
2022 |
Publication |
IET Computer Vision |
Abbreviated Journal |
IETCV |
|
| |
Volume  |
16 |
Issue |
1 |
Pages |
50-66 |
|
| |
Keywords |
Computer vision; data acquisition; human computer interaction; learning (artificial intelligence); pose estimation |
|
| |
Abstract |
Despite recent advances in 3D pose estimation of human hands, especially thanks to the advent of CNNs and depth cameras, this task is still far from being solved. This is mainly due to the highly non-linear dynamics of fingers, which make hand model training a challenging task. In this paper, we exploit a novel hierarchical tree-like structured CNN, in which branches are trained to become specialized in predefined subsets of hand joints, called local poses. We further fuse local pose features, extracted from hierarchical CNN branches, to learn higher order dependencies among joints in the final pose by end-to-end training. Lastly, the loss function used is also defined to incorporate appearance and physical constraints about doable hand motion and deformation. Finally, we introduce a non-rigid data augmentation approach to increase the amount of training depth data. Experimental results suggest that feeding a tree-shaped CNN, specialized in local poses, into a fusion network for modeling joints correlations and dependencies, helps to increase the precision of final estimations, outperforming state-of-the-art results on NYU and SyntheticHand datasets. |
|
| |
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; ISE; 600.098; 600.119 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ MEB2022 |
Serial |
3652 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Sergio Escalera; Vassilis Athitsos; Isabelle Guyon |
|
| |
Title |
Challenges in multimodal gesture recognition |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
Journal of Machine Learning Research |
Abbreviated Journal |
JMLR |
|
| |
Volume |
17 |
Issue |
|
Pages |
1-54 |
|
| |
Keywords |
Gesture Recognition; Time Series Analysis; Multimodal Data Analysis; Computer Vision; Pattern Recognition; Wearable sensors; Infrared Cameras; KinectTM |
|
| |
Abstract |
This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011-2015. We began right at the start of the KinectTMrevolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands
of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
Zhuowen Tu |
|
| |
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;MILAB; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ EAG2016 |
Serial |
2764 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Penny Tarling; Mauricio Cantor; Albert Clapes; Sergio Escalera |
|
| |
Title |
Deep learning with self-supervision and uncertainty regularization to count fish in underwater images |
Type |
Journal Article |
| |
Year |
2022 |
Publication |
PloS One |
Abbreviated Journal |
Plos |
|
| |
Volume |
17 |
Issue |
5 |
Pages |
e0267759 |
|
| |
Keywords |
|
|
| |
Abstract |
Effective conservation actions require effective population monitoring. However, accurately counting animals in the wild to inform conservation decision-making is difficult. Monitoring populations through image sampling has made data collection cheaper, wide-reaching and less intrusive but created a need to process and analyse this data efficiently. Counting animals from such data is challenging, particularly when densely packed in noisy images. Attempting this manually is slow and expensive, while traditional computer vision methods are limited in their generalisability. Deep learning is the state-of-the-art method for many computer vision tasks, but it has yet to be properly explored to count animals. To this end, we employ deep learning, with a density-based regression approach, to count fish in low-resolution sonar images. We introduce a large dataset of sonar videos, deployed to record wild Lebranche mullet schools (Mugil liza), with a subset of 500 labelled images. We utilise abundant unlabelled data in a self-supervised task to improve the supervised counting task. For the first time in this context, by introducing uncertainty quantification, we improve model training and provide an accompanying measure of prediction uncertainty for more informed biological decision-making. Finally, we demonstrate the generalisability of our proposed counting framework through testing it on a recent benchmark dataset of high-resolution annotated underwater images from varying habitats (DeepFish). From experiments on both contrasting datasets, we demonstrate our network outperforms the few other deep learning models implemented for solving this task. By providing an open-source framework along with training data, our study puts forth an efficient deep learning template for crowd counting aquatic animals thereby contributing effective methods to assess natural populations from the ever-increasing visual data. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Public Library of Science |
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 @ TCC2022 |
Serial |
3743 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
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 |
Type |
Journal Article |
| |
Year |
2022 |
Publication |
IEEE Transactions on Information Forensics and Security |
Abbreviated Journal |
TIForensicSEC |
|
| |
Volume |
17 |
Issue |
|
Pages |
2497 - 2507 |
|
| |
Keywords |
|
|
| |
Abstract |
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. |
|
| |
Address |
|
|
| |
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 |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
HuPBA |
Approved |
no |
|
| |
Call Number |
Admin @ si @ LZY2022 |
Serial |
3778 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Eloi Puertas; Sergio Escalera; Oriol Pujol |
|
| |
Title |
Generalized Multi-scale Stacked Sequential Learning for Multi-class Classification |
Type |
Journal Article |
| |
Year |
2015 |
Publication |
Pattern Analysis and Applications |
Abbreviated Journal |
PAA |
|
| |
Volume |
18 |
Issue |
2 |
Pages |
247-261 |
|
| |
Keywords |
Stacked sequential learning; Multi-scale; Error-correct output codes (ECOC); Contextual classification |
|
| |
Abstract |
In many classification problems, neighbor data labels have inherent sequential relationships. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In this paper, we revise the multi-scale sequential learning approach (MSSL) for applying it in the multi-class case (MMSSL). We introduce the error-correcting output codesframework in the MSSL classifiers and propose a formulation for calculating confidence maps from the margins of the base classifiers. In addition, we propose a MMSSL compression approach which reduces the number of features in the extended data set without a loss in performance. The proposed methods are tested on several databases, showing significant performance improvement compared to classical approaches. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer-Verlag |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1433-7541 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
HuPBA;MILAB |
Approved |
no |
|
| |
Call Number |
Admin @ si @ PEP2013 |
Serial |
2251 |
|
| Permanent link to this record |
