2 code implementations • 20 Mar 2023 • Weihuang Liu, Xi Shen, Chi-Man Pun, Xiaodong Cun
Different from the previous visual prompting which is typically a dataset-level implicit embedding, our key insight is to enforce the tunable parameters focusing on the explicit visual content from each individual image, i. e., the features from frozen patch embeddings and the input's high-frequency components.
1 code implementation • 15 Jan 2023 • Jianrong Zhang, Yangsong Zhang, Xiaodong Cun, Shaoli Huang, Yong Zhang, Hongwei Zhao, Hongtao Lu, Xi Shen
Additionally, we conduct analyses on HumanML3D and observe that the dataset size is a limitation of our approach.
Ranked #1 on
Motion Synthesis
on HumanML3D
1 code implementation • 22 Nov 2022 • Wenxuan Zhang, Xiaodong Cun, Xuan Wang, Yong Zhang, Xi Shen, Yu Guo, Ying Shan, Fei Wang
We present SadTalker, which generates 3D motion coefficients (head pose, expression) of the 3DMM from audio and implicitly modulates a novel 3D-aware face render for talking head generation.
no code implementations • 1 Sep 2022 • Yangtao Wang, Xi Shen, Yuan Yuan, Yuming Du, Maomao Li, Shell Xu Hu, James L Crowley, Dominique Vaufreydaz
This method also achieves competitive results for unsupervised video object segmentation tasks with the DAVIS, SegTV2, and FBMS datasets.
1 code implementation • 25 Jul 2022 • Yingyi Chen, Xi Shen, Yahui Liu, Qinghua Tao, Johan A. K. Suykens
In this paper, we explore solving jigsaw puzzle as a self-supervised auxiliary loss in ViT for image classification, named Jigsaw-ViT.
Ranked #1 on
Learning with noisy labels
on ANIMAL
1 code implementation • 4 Jul 2022 • Wen Guo, Yuming Du, Xi Shen, Vincent Lepetit, Xavier Alameda-Pineda, Francesc Moreno-Noguer
This paper tackles the problem of human motion prediction, consisting in forecasting future body poses from historically observed sequences.
1 code implementation • 27 Jun 2022 • Yingyi Chen, Shell Xu Hu, Xi Shen, Chunrong Ai, Johan A. K. Suykens
This decomposition provides three insights: (i) it shows that over-fitting is indeed an issue for learning with noisy labels; (ii) through an information bottleneck formulation, it explains why the proposed feature compression helps in combating label noise; (iii) it gives explanations on the performance boost brought by incorporating compression regularization into Co-teaching.
Ranked #7 on
Learning with noisy labels
on ANIMAL
1 code implementation • CVPR 2022 • Yangtao Wang, Xi Shen, Shell Hu, Yuan Yuan, James Crowley, Dominique Vaufreydaz
For unsupervised saliency detection, we improve IoU for 4. 9%, 5. 2%, 12. 9% on ECSSD, DUTS, DUT-OMRON respectively compared to previous state of the art.
Ranked #1 on
Weakly-Supervised Object Localization
on CUB-200-2011
(Top-1 Localization Accuracy metric)
no code implementations • NeurIPS 2021 • Xi Shen, Yang Xiao, Shell Hu, Othman Sbai, Mathieu Aubry
In the problems of image retrieval and few-shot classification, the mainstream approaches focus on learning a better feature representation.
1 code implementation • 29 Oct 2021 • Xi Shen, Alexei A. Efros, Armand Joulin, Mathieu Aubry
The goal of this work is to efficiently identify visually similar patterns in images, e. g. identifying an artwork detail copied between an engraving and an oil painting, or recognizing parts of a night-time photograph visible in its daytime counterpart.
no code implementations • 18 Aug 2021 • Ryad Kaoua, Xi Shen, Alexandra Durr, Stavros Lazaris, David Picard, Mathieu Aubry
For an historian, the first step in studying their evolution in a corpus of similar manuscripts is to identify which ones correspond to each other.
1 code implementation • 28 Apr 2021 • Yingyi Chen, Xi Shen, Shell Xu Hu, Johan A. K. Suykens
On Clothing1M, our approach obtains 74. 9% accuracy which is slightly better than that of DivideMix.
Ranked #10 on
Image Classification
on Clothing1M
(using extra training data)
2 code implementations • ICLR 2020 • Shell Xu Hu, Pablo G. Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil D. Lawrence, Andreas Damianou
The evidence lower bound of the marginal log-likelihood of empirical Bayes decomposes as a sum of local KL divergences between the variational posterior and the true posterior on the query set of each task.
Ranked #11 on
Few-Shot Image Classification
on CIFAR-FS 5-way (1-shot)
1 code implementation • ECCV 2020 • Xi Shen, François Darmon, Alexei A. Efros, Mathieu Aubry
Coarse alignment is performed using RANSAC on off-the-shelf deep features.
1 code implementation • 27 Aug 2019 • Xi Shen, Ilaria Pastrolin, Oumayma Bounou, Spyros Gidaris, Marc Smith, Olivier Poncet, Mathieu Aubry
Historical watermark recognition is a highly practical, yet unsolved challenge for archivists and historians.
no code implementations • ICLR 2019 • Yuan Yuan, Yueming Lyu, Xi Shen, Ivor W. Tsang, Dit-yan Yeung
The MAAN employs a novel marginalized average aggregation (MAA) module and learns a set of latent discriminative probabilities in an end-to-end fashion.
Ranked #7 on
Weakly Supervised Action Localization
on ActivityNet-1.3
(mAP@0.5 metric)
Weakly Supervised Action Localization
Weakly-supervised Learning
+2
1 code implementation • CVPR 2019 • Xi Shen, Alexei A. Efros, Mathieu Aubry
Our goal in this paper is to discover near duplicate patterns in large collections of artworks.