Yehansen Chen

Yehansen Chen

Graduate Student, China University of Geosciences

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Pages

Posts

Future Blog Post

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

less than 1 minute read

Published:

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portfolio

publications

Neural Feature Search for RGB-Infrared Person Re-Identification

Published in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, 2021

This paper is about an automated feature selection method for RGB-Infrared Person Re-Identification.

Recommended citation: Chen, Yehansen, et al. "Neural Feature Search for RGB-Infrared Person Re-Identification." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Neural_Feature_Search_for_RGB-Infrared_Person_Re-Identification_CVPR_2021_paper.html

talks

Nerual Feature Search for RGB-Infrared Person Re-Identification

Published:

RGB-Infrared person re-identification (RGB-IR ReID) is a challenging cross-modality retrieval problem, which aims at matching the person-of-interest over visible and infrared camera views. Most existing works achieve performance gains through manually-designed feature selection modules, which often require significant domain knowledge and rich experience. In this paper, we study a general paradigm, termed Neural Feature Search (NFS), to automate the process of feature selection. Specifically, NFS combines a dual-level feature search space and a differentiable search strategy to jointly select identity-related cues in coarse-grained channels and fine-grained spatial pixels. This combination allows NFS to adaptively filter background noises and concentrate on informative parts of human bodies in a data-driven manner. Moreover, a cross-modality contrastive optimization scheme further guides NFS to search features that can minimize modality discrepancy whilst maximizing inter-class distance. Extensive experiments on mainstream benchmarks demonstrate that our method outperforms state-of-the-arts, especially achieving better performance on the RegDB dataset with significant improvement of 11.20% and 8.64% in Rank-1 and mAP, respectively.

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

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