Transformer-Based Person Search with High-Frequency Augmentation and Multi-Wave Mixing

• URL: http://arxiv.org/abs/2506.23202v1
• Date: Sun, 29 Jun 2025 12:08:26 GMT
• Title: Transformer-Based Person Search with High-Frequency Augmentation and Multi-Wave Mixing
• Authors: Qilin Shu, Qixian Zhang, Qi Zhang, Hongyun Zhang, Duoqian Miao, Cairong Zhao,
• Abstract summary: We propose a novel High-frequency Augmentation and Multi-Wave mixing (HAMW) method for person search.<n>HamW is designed to enhance the discriminative feature extraction capabilities of transformers.<n>HamW achieves state-of-the-art performance on both the CUHK-SYSU and PRW datasets.
• Score: 18.871765626140782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The person search task aims to locate a target person within a set of scene images. In recent years, transformer-based models in this field have made some progress. However, they still face three primary challenges: 1) the self-attention mechanism tends to suppress high-frequency components in the features, which severely impacts model performance; 2) the computational cost of transformers is relatively high. To address these issues, we propose a novel High-frequency Augmentation and Multi-Wave mixing (HAMW) method for person search. HAMW is designed to enhance the discriminative feature extraction capabilities of transformers while reducing computational overhead and improving efficiency. Specifically, we develop a three-stage framework that progressively optimizes both detection and re-identification performance. Our model enhances the perception of high-frequency features by learning from augmented inputs containing additional high-frequency components. Furthermore, we replace the self-attention layers in the transformer with a strategy based on multi-level Haar wavelet fusion to capture multi-scale features. This not only lowers the computational complexity but also alleviates the suppression of high-frequency features and enhances the ability to exploit multi-scale information. Extensive experiments demonstrate that HAMW achieves state-of-the-art performance on both the CUHK-SYSU and PRW datasets.

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