Search Results for author: Xumeng Han

Found 6 papers, 4 papers with code

CPR++: Object Localization via Single Coarse Point Supervision

2 code implementations30 Jan 2024 Xuehui Yu, Pengfei Chen, Kuiran Wang, Xumeng Han, Guorong Li, Zhenjun Han, Qixiang Ye, Jianbin Jiao

CPR reduces the semantic variance by selecting a semantic centre point in a neighbourhood region to replace the initial annotated point.

Object Object Localization

P2Seg: Pointly-supervised Segmentation via Mutual Distillation

no code implementations18 Jan 2024 Zipeng Wang, Xuehui Yu, Xumeng Han, Wenwen Yu, Zhixun Huang, Jianbin Jiao, Zhenjun Han

Nevertheless, weakly supervised semantic segmentation methods are proficient in utilizing intra-class feature consistency to capture the boundary contours of the same semantic regions.

Box-supervised Instance Segmentation Segmentation +2

Boosting Segment Anything Model Towards Open-Vocabulary Learning

1 code implementation6 Dec 2023 Xumeng Han, Longhui Wei, Xuehui Yu, Zhiyang Dou, Xin He, Kuiran Wang, Zhenjun Han, Qi Tian

The recent Segment Anything Model (SAM) has emerged as a new paradigmatic vision foundation model, showcasing potent zero-shot generalization and flexible prompting.

Object Object Localization +2

P2RBox: A Single Point is All You Need for Oriented Object Detection

no code implementations22 Nov 2023 Guangming Cao, Xuehui Yu, Wenwen Yu, Xumeng Han, Xue Yang, Guorong Li, Jianbin Jiao, Zhenjun Han

In this study, we introduce the P2RBox network, which leverages point annotations and a mask generator to create mask proposals, followed by filtration through our Inspector Module and Constrainer Module.

Object object-detection +2

Rethinking Sampling Strategies for Unsupervised Person Re-identification

2 code implementations7 Jul 2021 Xumeng Han, Xuehui Yu, Guorong Li, Jian Zhao, Gang Pan, Qixiang Ye, Jianbin Jiao, Zhenjun Han

While extensive research has focused on the framework design and loss function, this paper shows that sampling strategy plays an equally important role.

Pseudo Label Representation Learning +1

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