activity
20122023
most citedDiscriminative Feature Learning with Foreground Attention for Person Re-Identification

71 citations · 282 across the 21 of their papers we have counts for

collaborators

21 papers

cs.LG2023

Understanding the Overfitting of the Episodic Meta-training

Siqi Hui, Sanping Zhou, Ye deng +1

Despite the success of two-stage few-shot classification methods, in the episodic meta-training stage, the model suffers severe overfitting. We hypothesize that it is caused by ove…

cs.CV2023★ 57 cited

T-former: An Efficient Transformer for Image Inpainting

Ye Deng, Siqi Hui, Sanping Zhou +2

Benefiting from powerful convolutional neural networks (CNNs), learning-based image inpainting methods have made significant breakthroughs over the years. However, some nature of C…

cs.CV2023

Pseudo Labels Refinement with Intra-camera Similarity for Unsupervised Person Re-identification

Pengna Li, Kangyi Wu, Sanping Zhou. Qianxin Huang +1

Unsupervised person re-identification (Re-ID) aims to retrieve person images across cameras without any identity labels. Most clustering-based methods roughly divide image features…

cs.CV2021★ 1 cited

Auxiliary Loss Reweighting for Image Inpainting

Siqi Hui, Sanping Zhou, Ye Deng +2

Image Inpainting is a task that aims to fill in missing regions of corrupted images with plausible contents. Recent inpainting methods have introduced perceptual and style losses a…

cs.CV2021★ 3 cited

Memory-Free Generative Replay For Class-Incremental Learning

Xiaomeng Xin, Yiran Zhong, Yunzhong Hou +2

Regularization-based methods are beneficial to alleviate the catastrophic forgetting problem in class-incremental learning. With the absence of old task images, they often assume t…

cs.CV2020★ 1 cited

Teacher-Student Asynchronous Learning with Multi-Source Consistency for Facial Landmark Detection

Rongye Meng, Sanping Zhou, Xingyu Wan +2

Due to the high annotation cost of large-scale facial landmark detection tasks in videos, a semi-supervised paradigm that uses self-training for mining high-quality pseudo-labels t…