基于Prompt-S6和语义感知知识引导的多模态目标重识别
Multi-Modal Object Re-Identification with Prompt-S6 and Semantic-Aware Knowledge Guidance
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中文总结 AI 辅助
研究多模态目标重识别问题，提出基于Prompt-S6和语义感知知识引导的PRISM框架，设计语义驱动令牌剪枝和渐进融合网络两个关键组件，有效抑制背景干扰，实现三模态对齐，提升多模态目标重识别的有效性和效率。
中文摘要 AI 辅助
多模态目标重识别旨在通过整合多模态的互补信息来检索特定目标。现有方法无法有效抑制背景干扰或实现三模态对齐，且计算复杂度高。为此提出PRISM框架，基于Prompt-S6和语义感知知识引导。设计了语义驱动令牌剪枝和渐进融合网络两个关键组件，可抑制背景噪声、实现三模态对齐并充分利用模态互补性，实验验证了该方法的有效性和效率。
英文摘要
Multi-modal object Re-Identification (ReID) aims to retrieve specific objects by integrating complementary information from multiple modalities. However, existing multi-modal ReID methods do not effectively address background interference suppression or achieve tri-modal alignment, instead focusing on pairwise feature fusion. Moreover, many current aggregation approaches suffer from high computational complexity. To address these limitations, we propose PRISM, a novel multi-modal ReID framework built upon Prompt-S6 (PS6) and semantic-aware knowledge guidance. PS6 maintains the linear complexity and strong sequence modeling capability of Mamba while enabling efficient cross-modal interaction. Leveraging these advantages, we design two key components: Semantic-Driven Token Pruning (SDTP) and Progressive Fusion Network (PFN). Parsing semantic priors from the segmentation foundation models, the SDTP then leverages these priors and applies dynamic token pruning to suppress background noise and refine feature representations. The PFN progressively aggregates multi-modal features to achieve tri-modal alignment and fully exploit modality complementarity. With the proposed modules, PRISM generates more robust multi-modal representations under complex scenarios. Extensive experiments on four multi-modal object ReID benchmarks demonstrate the effectiveness and efficiency of our approach. The source code is available at https://github.com/zw-absin/PRISM.
