Visible-infrared person re-identification via specific and shared representations learning
-
Published:2023-12-05
Issue:1
Volume:1
Page:
-
ISSN:2731-9008
-
Container-title:Visual Intelligence
-
language:en
-
Short-container-title:Vis. Intell.
Author:
Zheng AihuaORCID, Liu JuncongORCID, Wang ZiORCID, Huang LiliORCID, Li ChenglongORCID, Yin BingORCID
Abstract
AbstractThe primary goal of visible-infrared person re-identification (VI-ReID) is to match pedestrian photos obtained during the day and night. The majority of existing methods simply generate auxiliary modalities to reduce the modality discrepancy for cross-modality matching. They capture modality-invariant representations but ignore the extraction of modality-specific representations that can aid in distinguishing among various identities of the same modality. To alleviate these issues, this work provides a novel specific and shared representations learning (SSRL) model for VI-ReID to learn modality-specific and modality-shared representations. We design a shared branch in SSRL to bridge the image-level gap and learn modality-shared representations, while a specific branch retains the discriminative information of visible images to learn modality-specific representations. In addition, we propose intra-class aggregation and inter-class separation learning strategies to optimize the distribution of feature embeddings at a fine-grained level. Extensive experimental results on two challenging benchmark datasets, SYSU-MM01 and RegDB, demonstrate the superior performance of SSRL over state-of-the-art methods.
Funder
National Key R&D Program of China National Natural Science Foundation of China University Synergy Innovation Program of Anhui Province Natural Science Foundation of Anhui Province Natural Science Foundation of Anhui Higher Education Institution
Publisher
Springer Science and Business Media LLC
Reference57 articles.
1. Ye, M., Shen, J., Lin, G., Xiang, T., Shao, L., & Hoi, S. C. (2021). Deep learning for person re-identification: a survey and outlook. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(6), 2872–2893. 2. Zhai, Y., Lu, S., Ye, Q., Shan, X., Chen, J., Ji, R., et al. (2020). Ad-cluster: augmented discriminative clustering for domain adaptive person re-identification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 9021–9030). Piscataway: IEEE. 3. Liu, F., & Zhang, L. (2019). View confusion feature learning for person re-identification. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 6639–6648). Piscataway: IEEE. 4. Liu, M., Qu, L., Nie, L., Liu, M., Duan, L., & Chen, B. (2020). Iterative local-global collaboration learning towards one-shot video person re-identification. IEEE Transactions on Image Processing, 29, 9360–9372. 5. Zahra, A., Perwaiz, N., Shahzad, M., & Fraz, M. M. (2023). Person re-identification: a retrospective on domain specific open challenges and future trends. Pattern Recognition, 142, 109669.
|
|