activity
20172025
most citedT-former: An Efficient Transformer for Image Inpainting

57 citations · 142 across the 13 of their papers we have counts for

collaborators
Showing cs.CVShow all

13 papers · 1 filter

cs.CV2025

FreqGRL: Suppressing Low-Frequency Bias and Mining High-Frequency Knowledge for Cross-Domain Few-Shot Learning

Siqi Hui, Sanping Zhou, Ye deng +2

Cross-domain few-shot learning (CD-FSL) aims to recognize novel classes with only a few labeled examples under significant domain shifts. While recent approaches leverage a limited…

cs.CV2025

REGNav: Room Expert Guided Image-Goal Navigation

Pengna Li, Kangyi Wu, Jingwen Fu +1

Image-goal navigation aims to steer an agent towards the goal location specified by an image. Most prior methods tackle this task by learning a navigation policy, which extracts vi…

cs.CV2024

Camera-aware Label Refinement for Unsupervised Person Re-identification

Pengna Li, Kangyi Wu, Wenli Huang +2

Unsupervised person re-identification aims to retrieve images of a specified person without identity labels. Many recent unsupervised Re-ID approaches adopt clustering-based method…

cs.CV202357 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.CV20201 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…