Gradient-Attention Guided Dual-Masking Synergetic Framework for Robust Text-based Person Retrieval

• URL: http://arxiv.org/abs/2509.09118v1
• Date: Thu, 11 Sep 2025 03:06:22 GMT
• Title: Gradient-Attention Guided Dual-Masking Synergetic Framework for Robust Text-based Person Retrieval
• Authors: Tianlu Zheng, Yifan Zhang, Xiang An, Ziyong Feng, Kaicheng Yang, Qichuan Ding,
• Abstract summary: This work advances Contrastive Language-Image Pre-training (CLIP) for person representation learning.<n>We develop a noise-resistant data construction pipeline that leverages the in-context learning capabilities of MLLMs.<n>We introduce the GA-DMS framework, which improves cross-modal alignment by adaptively masking noisy textual tokens.
• Score: 15.126709823382539
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although Contrastive Language-Image Pre-training (CLIP) exhibits strong performance across diverse vision tasks, its application to person representation learning faces two critical challenges: (i) the scarcity of large-scale annotated vision-language data focused on person-centric images, and (ii) the inherent limitations of global contrastive learning, which struggles to maintain discriminative local features crucial for fine-grained matching while remaining vulnerable to noisy text tokens. This work advances CLIP for person representation learning through synergistic improvements in data curation and model architecture. First, we develop a noise-resistant data construction pipeline that leverages the in-context learning capabilities of MLLMs to automatically filter and caption web-sourced images. This yields WebPerson, a large-scale dataset of 5M high-quality person-centric image-text pairs. Second, we introduce the GA-DMS (Gradient-Attention Guided Dual-Masking Synergetic) framework, which improves cross-modal alignment by adaptively masking noisy textual tokens based on the gradient-attention similarity score. Additionally, we incorporate masked token prediction objectives that compel the model to predict informative text tokens, enhancing fine-grained semantic representation learning. Extensive experiments show that GA-DMS achieves state-of-the-art performance across multiple benchmarks.

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