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Person Re-Identification via Pyramid Multipart Features and Multi-Attention Framework
2022
Big Data and Cognitive Computing
Video-based person re-identification has become quite attractive due to its importance in many vision surveillance problems. It is a challenging topic due to the inter/intra changes, occlusion, and pose variations involved. In this paper, we propose a pyramid-attentive framework that relies on multi-part features and multiple attention to aggregate features of multi-levels and learns attention-based representations of persons through various aspects. Self-attention is used to strengthen the
doi:10.3390/bdcc6010020
fatcat:tgitv7wy4rdhzhci35evmwkvhu