|
Records |
Links |
|
Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Kai Wang; Xialei Liu; Andrew Bagdanov; Luis Herranz; Shangling Jui; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition |
Type |
Conference Article |
|
Year |
2022 |
Publication |
CVPR 2022 Workshop on Continual Learning (CLVision, 3rd Edition) |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
3728-3738 |
|
|
Keywords |
Training; Computer vision; Image recognition; Upper bound; Conferences; Pattern recognition; Task analysis |
|
|
Abstract |
In this paper we consider the problem of incremental meta-learning in which classes are presented incrementally in discrete tasks. We propose Episodic Replay Distillation (ERD), that mixes classes from the current task with exemplars from previous tasks when sampling episodes for meta-learning. To allow the training to benefit from a large as possible variety of classes, which leads to more gener-
alizable feature representations, we propose the cross-task meta loss. Furthermore, we propose episodic replay distillation that also exploits exemplars for improved knowledge distillation. Experiments on four datasets demonstrate that ERD surpasses the state-of-the-art. In particular, on the more challenging one-shot, long task sequence scenarios, we reduce the gap between Incremental Meta-Learning and
the joint-training upper bound from 3.5% / 10.1% / 13.4% / 11.7% with the current state-of-the-art to 2.6% / 2.9% / 5.0% / 0.2% with our method on Tiered-ImageNet / Mini-ImageNet / CIFAR100 / CUB, respectively. |
|
|
Address |
New Orleans, USA; 20 June 2022 |
|
|
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 |
CVPRW |
|
|
Notes |
LAMP; 600.147 |
Approved |
no |
|
|
Call Number |
Admin @ si @ WLB2022 |
Serial |
3686 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Kai Wang; Luis Herranz; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
Continual learning in cross-modal retrieval |
Type |
Conference Article |
|
Year |
2021 |
Publication |
2nd CLVISION workshop |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
3628-3638 |
|
|
Keywords |
|
|
|
Abstract |
Multimodal representations and continual learning are two areas closely related to human intelligence. The former considers the learning of shared representation spaces where information from different modalities can be compared and integrated (we focus on cross-modal retrieval between language and visual representations). The latter studies how to prevent forgetting a previously learned task when learning a new one. While humans excel in these two aspects, deep neural networks are still quite limited. In this paper, we propose a combination of both problems into a continual cross-modal retrieval setting, where we study how the catastrophic interference caused by new tasks impacts the embedding spaces and their cross-modal alignment required for effective retrieval. We propose a general framework that decouples the training, indexing and querying stages. We also identify and study different factors that may lead to forgetting, and propose tools to alleviate it. We found that the indexing stage pays an important role and that simply avoiding reindexing the database with updated embedding networks can lead to significant gains. We evaluated our methods in two image-text retrieval datasets, obtaining significant gains with respect to the fine tuning baseline. |
|
|
Address |
Virtual; June 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 |
CVPRW |
|
|
Notes |
LAMP; 600.120; 600.141; 600.147; 601.379 |
Approved |
no |
|
|
Call Number |
Admin @ si @ WHW2021 |
Serial |
3566 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Kai Wang; Luis Herranz; Anjan Dutta; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
Bookworm continual learning: beyond zero-shot learning and continual learning |
Type |
Conference Article |
|
Year |
2020 |
Publication |
Workshop TASK-CV 2020 |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
We propose bookworm continual learning(BCL), a flexible setting where unseen classes can be inferred via a semantic model, and the visual model can be updated continually. Thus BCL generalizes both continual learning (CL) and zero-shot learning (ZSL). We also propose the bidirectional imagination (BImag) framework to address BCL where features of both past and future classes are generated. We observe that conditioning the feature generator on attributes can actually harm the continual learning ability, and propose two variants (joint class-attribute conditioning and asymmetric generation) to alleviate this problem. |
|
|
Address |
Virtual; August 2020 |
|
|
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 |
ECCVW |
|
|
Notes |
LAMP; 600.141; 600.120 |
Approved |
no |
|
|
Call Number |
Admin @ si @ WHD2020 |
Serial |
3466 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Kai Wang; Joost Van de Weijer; Luis Herranz |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
ACAE-REMIND for online continual learning with compressed feature replay |
Type |
Journal Article |
|
Year |
2021 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
|
|
Volume |
150 |
Issue |
|
Pages |
122-129 |
|
|
Keywords |
online continual learning; autoencoders; vector quantization |
|
|
Abstract |
Online continual learning aims to learn from a non-IID stream of data from a number of different tasks, where the learner is only allowed to consider data once. Methods are typically allowed to use a limited buffer to store some of the images in the stream. Recently, it was found that feature replay, where an intermediate layer representation of the image is stored (or generated) leads to superior results than image replay, while requiring less memory. Quantized exemplars can further reduce the memory usage. However, a drawback of these methods is that they use a fixed (or very intransigent) backbone network. This significantly limits the learning of representations that can discriminate between all tasks. To address this problem, we propose an auxiliary classifier auto-encoder (ACAE) module for feature replay at intermediate layers with high compression rates. The reduced memory footprint per image allows us to save more exemplars for replay. In our experiments, we conduct task-agnostic evaluation under online continual learning setting and get state-of-the-art performance on ImageNet-Subset, CIFAR100 and CIFAR10 dataset. |
|
|
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 |
LAMP; 600.147; 601.379; 600.120; 600.141 |
Approved |
no |
|
|
Call Number |
Admin @ si @ WWH2021 |
Serial |
3575 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Kai Wang; Fei Yang; Shiqi Yang; Muhammad Atif Butt; Joost Van de Weijer |
![goto web page url](img/www.gif)
|
|
Title |
Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing |
Type |
Conference Article |
|
Year |
2023 |
Publication |
37th Annual Conference on Neural Information Processing Systems |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
Poster |
|
|
Address |
New Orleans; USA; December 2023 |
|
|
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 |
NEURIPS |
|
|
Notes |
LAMP |
Approved |
no |
|
|
Call Number |
Admin @ si @ WYY2023 |
Serial |
3935 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Kai Wang; Fei Yang; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
Attention Distillation: self-supervised vision transformer students need more guidance |
Type |
Conference Article |
|
Year |
2022 |
Publication |
33rd British Machine Vision Conference |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
Self-supervised learning has been widely applied to train high-quality vision transformers. Unleashing their excellent performance on memory and compute constraint devices is therefore an important research topic. However, how to distill knowledge from one self-supervised ViT to another has not yet been explored. Moreover, the existing self-supervised knowledge distillation (SSKD) methods focus on ConvNet based architectures are suboptimal for ViT knowledge distillation. In this paper, we study knowledge distillation of self-supervised vision transformers (ViT-SSKD). We show that directly distilling information from the crucial attention mechanism from teacher to student can significantly narrow the performance gap between both. In experiments on ImageNet-Subset and ImageNet-1K, we show that our method AttnDistill outperforms existing self-supervised knowledge distillation (SSKD) methods and achieves state-of-the-art k-NN accuracy compared with self-supervised learning (SSL) methods learning from scratch (with the ViT-S model). We are also the first to apply the tiny ViT-T model on self-supervised learning. Moreover, AttnDistill is independent of self-supervised learning algorithms, it can be adapted to ViT based SSL methods to improve the performance in future research. |
|
|
Address |
London; UK; November 2022 |
|
|
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 |
BMVC |
|
|
Notes |
LAMP; 600.147 |
Approved |
no |
|
|
Call Number |
Admin @ si @ WYW2022 |
Serial |
3793 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Kai Wang; Chenshen Wu; Andrew Bagdanov; Xialei Liu; Shiqi Yang; Shangling Jui; Joost Van de Weijer |
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification |
Type |
Conference Article |
|
Year |
2022 |
Publication |
33rd British Machine Vision Conference |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
Lifelong object re-identification incrementally learns from a stream of re-identification tasks. The objective is to learn a representation that can be applied to all tasks and that generalizes to previously unseen re-identification tasks. The main challenge is that at inference time the representation must generalize to previously unseen identities. To address this problem, we apply continual meta metric learning to lifelong object re-identification. To prevent forgetting of previous tasks, we use knowledge distillation and explore the roles of positive and negative pairs. Based on our observation that the distillation and metric losses are antagonistic, we propose to remove positive pairs from distillation to robustify model updates. Our method, called Distillation without Positive Pairs (DwoPP), is evaluated on extensive intra-domain experiments on person and vehicle re-identification datasets, as well as inter-domain experiments on the LReID benchmark. Our experiments demonstrate that DwoPP significantly outperforms the state-of-the-art. |
|
|
Address |
London; UK; November 2022 |
|
|
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 |
BMVC |
|
|
Notes |
LAMP; 600.147 |
Approved |
no |
|
|
Call Number |
Admin @ si @ WWB2022 |
Serial |
3794 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Kai Wang |
![find book details (via ISBN) isbn](img/isbn.gif)
|
|
Title |
Continual learning for hierarchical classification, few-shot recognition, and multi-modal learning |
Type |
Book Whole |
|
Year |
2022 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
Deep learning has drastically changed computer vision in the past decades and achieved great success in many applications, such as image classification, retrieval, detection, and segmentation thanks to the emergence of neural networks. Typically, for most applications, these networks are presented with examples from all tasks they are expected to perform. However, for many applications, this is not a realistic
scenario, and an algorithm is required to learn tasks sequentially. Continual learning proposes theory and methods for this scenario.
The main challenge for continual learning systems is called catastrophic forgetting and refers to a significant drop in performance on previous tasks. To tackle this problem, three main branches of methods have been explored to alleviate the forgetting in continual learning. They are regularization-based methods, rehearsalbased methods, and parameter isolation-based methods. However, most of them are focused on image classification tasks. Continual learning of many computer vision fields has still not been well-explored. Thus, in this thesis, we extend the continual learning knowledge to meta learning, we propose a method for the incremental learning of hierarchical relations for image classification, we explore image recognition in online continual learning, and study continual learning for cross-modal learning.
In this thesis, we explore the usage of image rehearsal when addressing the incremental meta learning problem. Observing that existingmethods fail to improve performance with saved exemplars, we propose to mix exemplars with current task data and episode-level distillation to overcome forgetting in incremental meta learning. Next, we study a more realistic image classification scenario where each class has multiple granularity levels. Only one label is present at any time, which requires the model to infer if the provided label has a hierarchical relation with any already known label. In experiments, we show that the estimated hierarchy information can be beneficial in both the training and inference stage.
For the online continual learning setting, we investigate the usage of intermediate feature replay. In this case, the training samples are only observed by the model only one time. Here we fix thememory buffer for feature replay and compare the effectiveness of saving features from different layers. Finally, we investigate multi-modal continual learning, where an image encoder is cooperating with a semantic branch. We consider the continual learning of both zero-shot learning and cross-modal retrieval problems. |
|
|
Address |
July, 2022 |
|
|
Corporate Author |
|
Thesis |
Ph.D. thesis |
|
|
Publisher |
|
Place of Publication |
|
Editor |
Luis Herranz;Joost Van de Weijer |
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
978-84-124793-2-4 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
LAMP |
Approved |
no |
|
|
Call Number |
Admin @ si @ Wan2022 |
Serial |
3714 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
JW Xiao; CB Zhang; J. Feng; Xialei Liu; Joost Van de Weijer; MM Cheng |
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
Endpoints Weight Fusion for Class Incremental Semantic Segmentation |
Type |
Conference Article |
|
Year |
2023 |
Publication |
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
7204-7213 |
|
|
Keywords |
|
|
|
Abstract |
Class incremental semantic segmentation (CISS) focuses on alleviating catastrophic forgetting to improve discrimination. Previous work mainly exploit regularization (e.g., knowledge distillation) to maintain previous knowledge in the current model. However, distillation alone often yields limited gain to the model since only the representations of old and new models are restricted to be consistent. In this paper, we propose a simple yet effective method to obtain a model with strong memory of old knowledge, named Endpoints Weight Fusion (EWF). In our method, the model containing old knowledge is fused with the model retaining new knowledge in a dynamic fusion manner, strengthening the memory of old classes in ever-changing distributions. In addition, we analyze the relation between our fusion strategy and a popular moving average technique EMA, which reveals why our method is more suitable for class-incremental learning. To facilitate parameter fusion with closer distance in the parameter space, we use distillation to enhance the optimization process. Furthermore, we conduct experiments on two widely used datasets, achieving the state-of-the-art performance. |
|
|
Address |
Vancouver; Canada; June 2023 |
|
|
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 |
CVPR |
|
|
Notes |
LAMP |
Approved |
no |
|
|
Call Number |
Admin @ si @ XZF2023 |
Serial |
3854 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Justine Giroux; Mohammad Reza Karimi Dastjerdi; Yannick Hold-Geoffroy; Javier Vazquez; Jean François Lalonde |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Towards a Perceptual Evaluation Framework for Lighting Estimation |
Type |
Conference Article |
|
Year |
2024 |
Publication |
Arxiv |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
rogress in lighting estimation is tracked by computing existing image quality assessment (IQA) metrics on images from standard datasets. While this may appear to be a reasonable approach, we demonstrate that doing so does not correlate to human preference when the estimated lighting is used to relight a virtual scene into a real photograph. To study this, we design a controlled psychophysical experiment where human observers must choose their preference amongst rendered scenes lit using a set of lighting estimation algorithms selected from the recent literature, and use it to analyse how these algorithms perform according to human perception. Then, we demonstrate that none of the most popular IQA metrics from the literature, taken individually, correctly represent human perception. Finally, we show that by learning a combination of existing IQA metrics, we can more accurately represent human preference. This provides a new perceptual framework to help evaluate future lighting estimation algorithms. |
|
|
Address |
Seattle; USA; June 2024 |
|
|
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 |
CVPR |
|
|
Notes |
MACO; CIC |
Approved |
no |
|
|
Call Number |
Admin @ si @ GDH2024 |
Serial |
3999 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Jun Wan; Yibing Zhao; Shuai Zhou; Isabelle Guyon; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture Recognition |
Type |
Conference Article |
|
Year |
2016 |
Publication |
29th IEEE Conference on Computer Vision and Pattern Recognition Worshops |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
In this paper, we present two large video multi-modal datasets for RGB and RGB-D gesture recognition: the ChaLearn LAP RGB-D Isolated Gesture Dataset (IsoGD)and the Continuous Gesture Dataset (ConGD). Both datasets are derived from the ChaLearn Gesture Dataset
(CGD) that has a total of more than 50000 gestures for the “one-shot-learning” competition. To increase the potential of the old dataset, we designed new well curated datasets composed of 249 gesture labels, and including 47933 gestures manually labeled the begin and end frames in sequences.Using these datasets we will open two competitions
on the CodaLab platform so that researchers can test and compare their methods for “user independent” gesture recognition. The first challenge is designed for gesture spotting
and recognition in continuous sequences of gestures while the second one is designed for gesture classification from segmented data. The baseline method based on the bag of visual words model is also presented. |
|
|
Address |
Las Vegas; USA; July 2016 |
|
|
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 |
CVPRW |
|
|
Notes |
HuPBA;MILAB; |
Approved |
no |
|
|
Call Number |
Admin @ si @ WZZ2016 |
Serial |
2771 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Jun Wan; Sergio Escalera; Gholamreza Anbarjafari; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon; Meysam Madadi; Juri Allik; Jelena Gorbova; Chi Lin; Yiliang Xie |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
Results and Analysis of ChaLearn LAP Multi-modal Isolated and ContinuousGesture Recognition, and Real versus Fake Expressed Emotions Challenges |
Type |
Conference Article |
|
Year |
2017 |
Publication |
Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
We analyze the results of the 2017 ChaLearn Looking at People Challenge at ICCV. The challenge comprised three tracks: (1) large-scale isolated (2) continuous gesture recognition, and (3) real versus fake expressed emotions tracks. It is the second round for both gesture recognition challenges, which were held first in the context of the ICPR 2016 workshop on “multimedia challenges beyond visual analysis”. In this second round, more participants joined the competitions, and the performances considerably improved compared to the first round. Particularly, the best recognition accuracy of isolated gesture recognition has improved from 56.90% to 67.71% in the IsoGD test set, and Mean Jaccard Index (MJI) of continuous gesture recognition has improved from 0.2869 to 0.6103 in the ConGD test set. The third track is the first challenge on real versus fake expressed emotion classification, including six emotion categories, for which a novel database was introduced. The first place was shared between two teams who achieved 67.70% averaged recognition rate on the test set. The data of the three tracks, the participants' code and method descriptions are publicly available to allow researchers to keep making progress in the field. |
|
|
Address |
Venice; Italy; October 2017 |
|
|
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 |
ICCVW |
|
|
Notes |
HUPBA; no menciona |
Approved |
no |
|
|
Call Number |
Admin @ si @ WEA2017 |
Serial |
3066 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Jun Wan; Sergio Escalera; Francisco Perales; Josef Kittler |
![goto web page url](img/www.gif)
|
|
Title |
Articulated Motion and Deformable Objects |
Type |
Journal Article |
|
Year |
2018 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
|
Volume |
79 |
Issue |
|
Pages |
55-64 |
|
|
Keywords |
|
|
|
Abstract |
This guest editorial introduces the twenty two papers accepted for this Special Issue on Articulated Motion and Deformable Objects (AMDO). They are grouped into four main categories within the field of AMDO: human motion analysis (action/gesture), human pose estimation, deformable shape segmentation, and face analysis. For each of the four topics, a survey of the recent developments in the field is presented. The accepted papers are briefly introduced in the context of this survey. They contribute novel methods, algorithms with improved performance as measured on benchmarking datasets, as well as two new datasets for hand action detection and human posture analysis. The special issue should be of high relevance to the reader interested in AMDO recognition and promote future research directions in the field. |
|
|
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; no proj |
Approved |
no |
|
|
Call Number |
Admin @ si @ WEP2018 |
Serial |
3126 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z. Li |
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Multi-modal Face Presentation Attach Detection |
Type |
Book Whole |
|
Year |
2020 |
Publication |
Synthesis Lectures on Computer Vision |
Abbreviated Journal |
|
|
|
Volume |
13 |
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
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 |
Approved |
no |
|
|
Call Number |
Admin @ si @ WGE2020 |
Serial |
3440 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li |
![goto web page url](img/www.gif)
|
|
Title |
Advances in Face Presentation Attack Detection |
Type |
Book Whole |
|
Year |
2023 |
Publication |
Advances in Face Presentation Attack Detection |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
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 |
Approved |
no |
|
|
Call Number |
Admin @ si @ WGE2023a |
Serial |
3955 |
|
Permanent link to this record |