思维链引导的多模态目标重识别
Chain-of-Thought Guided Multi-Modal Object Re-Identification
把 MLLM 的思维链文本推理作为锚框条件，在早期特征、跨模态一致性和决策三个层级约束视觉特征学习，为视觉-语言检索类任务提供了一种可复用的「文本逻辑引导视觉表征」范式。
原文依据 we propose CoT-ReID, a CoT-guided framework that injects the Multi-modal Large Language Models (MLLMs) reasoning into multi-modal ReID
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论随着视觉-语言模型的兴起，多模态重识别通过融合不同光谱与文本描述来检索特定目标。现有方法仅对图像-文本采用描述性表征学习，忽略了语义特征内在逻辑层级之间的关系。由于思维链能够提供文本逻辑上下文并增强大模型推理中的语义感知，我们提出 CoT-ReID，一个将多模态大语言模型推理注入多模态重识别的思维链引导框架。具体而言，我们模拟人类推理中视觉-文本联合的逻辑决策过程，利用思维链文本逻辑推理在早期、后期与决策三个层级引导视觉特征学习：在早期层级，我们将思维链层级推理的语义回逆嵌入视觉特征，以校准底层特征并强调视觉层级推理；接着，我们以思维链层级推理文本作为锚框条件，约束视觉跨模态语义的一致性；最后，通过思维链的层级推理过程，我们将经过逻辑推理的文本属性特征嵌入多模态决策，为选择判别性身份特征提供逻辑支撑。通过构建思维链文本基准与所提出的模块，我们的框架在复杂场景下生成更鲁棒的多模态特征。在四个数据集（RGBNT100、MSVR310、WMVeID863、RGBNT201）上的综合实验表明，我们的方法优于现有方法。代码将在论文录用后发布。
英文原文摘要 1651 字符
With the rise of visual-language models, multi-modal ReID retrieves specific targets by integrating different spectra and textual descriptions. Existing methods merely adopt descriptive representation learning for image-text, ignoring the relationships among the intrinsic logical hierarchies of semantic features. Since Chain-of-Thought (CoT) can provide textual logical context and enhance semantic perception in large-model reasoning, we propose CoT-ReID, a CoT-guided framework that injects the Multi-modal Large Language Models (MLLMs) reasoning into multi-modal ReID. Specifically, we simulate the joint visual-textual logical decision-making of human reasoning, leveraging CoT textual logical reasoning to guide visual feature learning at the early, late, and decision-making level: At the early level, we embed the semantic reversion of CoT hierarchical reasoning into visual features to calibrate bottom-level features and emphasize visual hierarchical reasoning. Next, we take CoT hierarchical reasoning text as an anchor condition to constrain the consistency of visual cross-modal semantics. Finally, through the hierarchical reasoning process of CoT, we embed logically reasoned text attribute features into multi-modal decision-making, providing logical support for selecting discriminative identity features. By constructing CoT textual benchmarks and our proposed modules, our framework generates more robust multi-modal features in complex scenarios. Comprehensive experiments on four datasets (RGBNT100, MSVR310, WMVeID863, RGBNT201) demonstrate that our method outperforms existing approaches. Code will be released upon acceptance.