Search Results for author: Xi Shen

Found 17 papers, 13 papers with code

Explicit Visual Prompting for Low-Level Structure Segmentations

2 code implementations20 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.

SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation

1 code implementation22 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.

Talking Head Generation

Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer

1 code implementation25 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.

Classification Domain Generalization +2

Back to MLP: A Simple Baseline for Human Motion Prediction

1 code implementation4 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.

Human motion prediction motion prediction

Compressing Features for Learning with Noisy Labels

1 code implementation27 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.

Feature Compression Feature Importance +2

Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut

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)

object-detection Object Discovery +5

Re-ranking for image retrieval and transductive few-shot classification

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.

Classification Few-Shot Learning +3

Learning Co-segmentation by Segment Swapping for Retrieval and Discovery

1 code implementation29 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.

Graph Clustering Object Discovery +3

Image Collation: Matching illustrations in manuscripts

no code implementations18 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.

Boosting Co-teaching with Compression Regularization for Label Noise

1 code implementation28 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)

Data Compression Learning with noisy labels +1

Empirical Bayes Transductive Meta-Learning with Synthetic Gradients

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.

Few-Shot Image Classification Meta-Learning +3

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