HAT: Hierarchical Aggregation Transformers for Person Re-identification

Understand

Recently, with the advance of deep Convolutional Neural Networks (CNNs), person Re-Identification (Re-ID) has witnessed great success in various applications.

• However, with limited receptive fields of CNNs, it is still challenging to extract discriminative representations in a global view for persons under non-overlapped cameras.
• Meanwhile, Transformers demonstrate strong abilities of modeling long-range dependencies for spatial and sequential data.
• In this work, we take advantages of both CNNs and Transformers, and propose a novel learning framework named Hierarchical Aggregation Transformer (HAT) for image-based person Re-ID with high performance.