Portfolio item number 1
Short description of portfolio item number 1
Posts by Collection
portfolio
Portfolio item number 2
Short description of portfolio item number 2
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
Self-Supervised Modality-Aware Multiple Granularity Pre-Training for RGB-Infrared Person Re-Identification
Published in Arxiv, 2021
This paper is about a self-supervised pre-training solution for RGB-Infrared person re-identification.
Recommended citation: Wan, Lin, et al. "Self-Supervised Modality-Aware Multiple Granularity Pre-Training for RGB-Infrared Person Re-Identification." arXiv preprint arXiv:2112.06147 (2021). https://arxiv.org/abs/2112.06147
A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study
Published in EclinicalMedicine, 20021
Detection + Classification for OCSCC lesions.
Recommended citation: Fu, Qiuyun, et al. "A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study." EClinicalMedicine 27 (2020): 100558. https://www.sciencedirect.com/science/article/pii/S2589537020303023
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
This is a description of a teaching experience. You can use markdown like any other post.