Self-attention-Based Dual-Branch Person Re-identification
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5591-2_18
Reference17 articles.
1. Sun, Y., et al.: Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part IV, pp. 501–518. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_30
2. Guo, J.Y., et al.: Beyond human parts: dual part-aligned representations for person re-identification. In: 2019 IEEE/CVF International Conference on Computer Vision, pp. 3641–3650. IEEE Computer Soc, Los Alamitos (2019)
3. Su, C., et al.: Pose-driven deep convolutional model for person re-identification, In: 2017 IEEE International Conference on Computer Vision, pp. 3980–3989. IEEE: New York (2019)
4. Liu, Y.T., et al.: Hierarchical feature aggregation from body parts for misalignment robust person re-identification. Appl. Sci.-Basel 9(11), 1–20 (2019)
5. Gong, T.T., et al.: Dynamically adaptive instance normalization and attention-aware incremental meta-learning for generalizable person re-identification. In: IEEE International Conference on Multimedia and Expo. Brisbane, AUSTRALIA (2023)
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