Enhancing Visible-Infrared Person Re-identification with Modality- and Instance-aware Visual Prompt Learning-Reference-Cited by-同舟云学术

Enhancing Visible-Infrared Person Re-identification with Modality- and Instance-aware Visual Prompt Learning

Published:2024-05-30 Issue: Volume:35 Page:579-588
ISSN:
Container-title:Proceedings of the 2024 International Conference on Multimedia Retrieval
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Author:

Wu Ruiqi¹^ORCID,Jiao Bingliang¹^ORCID,Wang Wenxuan¹^ORCID,Liu Meng²^ORCID,Wang Peng¹^ORCID

Affiliation:

1. School of Computer Science, Northwestern Polytechnical University & Ningbo Institute, Northwestern Polytechnical University, Xi'an, Shaanxi, China

2. Honors College, Northwestern Polytechnical University & School of Electronics and Information, Northwestern Polytechnical University, Xi'an, Shaanxi, China

Funder

National Natural Science Foundation of China

Natural Science Basic Research Program of Shaanxi Province

Shaanxi Provincial Key R&D Program

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3652583.3658109

Reference54 articles.

1. Hyojin Bahng, Ali Jahanian, Swami Sankaranarayanan, and Phillip Isola. 2022. Exploring Visual Prompts for Adapting Large-Scale Models. arXiv preprint arXiv:2203.17274 (2022).

2. Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems Vol. 33 (2020) 1877--1901.

3. Structure-Aware Positional Transformer for Visible-Infrared Person Re-Identification

4. AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition;Chen Shoufa;Proc. Advances in Neural Inf. Process. Syst.,2022

5. Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification