Pedestrian-specific Bipartite-aware Similarity Learning for Text-based Person Retrieval

Author:

Shen Fei1ORCID,Shu Xiangbo1ORCID,Du Xiaoyu1ORCID,Tang Jinhui1ORCID

Affiliation:

1. Nanjing University of Science and Technology, Nanjing, China

Funder

Natural Science Foundation of Jiangsu Province

2021 Jiangsu Shuangchuang (Mass Innovation and Entrepreneurship) Talent Program

National Natural Science Founda- tion of China

Publisher

ACM

Reference73 articles.

1. Cross-Modal Knowledge Adaptation for Language-Based Person Search

2. TIPCB: A simple but effective part-based convolutional baseline for text-based person search

3. Zefeng Ding , Changxing Ding , Zhiyin Shao , and Dacheng Tao . 2021. Semantically self-aligned network for text-to-image part-aware person re-identification. arXiv preprint arXiv:2107.12666 ( 2021 ). Zefeng Ding, Changxing Ding, Zhiyin Shao, and Dacheng Tao. 2021. Semantically self-aligned network for text-to-image part-aware person re-identification. arXiv preprint arXiv:2107.12666 (2021).

4. Neng Dong , Liyan Zhang , Shuanglin Yan , Hao Tang , and Jinhui Tang . 2023. Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification . arXiv preprint arXiv:2307.07187 ( 2023 ). Neng Dong, Liyan Zhang, Shuanglin Yan, Hao Tang, and Jinhui Tang. 2023. Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification. arXiv preprint arXiv:2307.07187 (2023).

5. Alexey Dosovitskiy , Lucas Beyer , Alexander Kolesnikov , Dirk Weissenborn , Xiaohua Zhai , Thomas Unterthiner , Mostafa Dehghani , Matthias Minderer , Georg Heigold , Sylvain Gelly , Jakob Uszkoreit , and Neil Houlsby . 2021 . An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale . In 9th International Conference on Learning Representations, ICLR. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In 9th International Conference on Learning Representations, ICLR.

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