VFI-ReID提出视觉反馈交互式行人检索新范式
Interactive Person Retrieval with On-the-Fly Visual Feedback
做行人重识别或多轮检索的研究者可以看看这篇。它把候选图片的视觉差异直接变成下一轮提问的依据，和只学固定提问顺序的旧 Inter-PR 方法思路明显不同，还附带了可用的 VFI-PEDES 数据集。
arXiv 论文提出视觉反馈交互式行人检索（VFI-PR）范式：检索到的候选行人图片会生成视觉反馈，作为后续提问和重排序的证据，突破了传统文本行人检索（TPR）一次性完整描述的假设。
为支撑该范式的训练与评估，作者构建了 VFI-PEDES 数据集及评估协议，并提出 VFI-ReID 框架。该框架包含动态累积证据检索器（DAR）和检索感知的问题优化两个核心模块，使提问策略随证据状态动态演化。
实验表明 VFI-ReID 在各交互阶段均稳定提升检索效果，超越现有 Inter-PR 基线方法。
Interactive Person Retrieval with On-the-Fly Visual Feedback
Traditional text-based person retrieval (TPR) assumes that a witness provides a complete description at once, whereas interactive person retrieval (Inter-PR) refines a partial description through question-answer exchanges. In this setting, the questioner inspects retrieved candidates to decide what evidence to request next, while the answerer sees only the witness-side memory and cannot report candidate-specific visual differences. Existing methods mainly learn offline question orderings, leaving this visual-feedback loop underexplored. To address this limitation, we introduce visual-feedback interactive person retrieval (VFI-PR), a new interactive retrieval paradigm in which retrieved candidates produce visual feedback that becomes evidence for subsequent questioning and re-ranking. To support training and evaluation in this setting, we construct VFI-PEDES, a dataset and evaluation protocol for visual-feedback interaction, and propose VFI-ReID, a framework that learns to use visual feedback during interactive person retrieval. Through the Dynamic Accumulated-evidence Retriever (DAR) and retrieval-aware question optimization, VFI-ReID adapts retrieval and question generation to evolving evidence states, turning visual feedback into complementary evidence for subsequent retrieval. Experiments show that VFI-ReID consistently improves retrieval across VFI-PR interaction stages, demonstrating the utility of visual feedback for interactive person retrieval and outperforming existing Inter-PR baselines.
This report is categorized under AITOP's "Papers" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include VFI-ReID, 行人重识别, 多模态检索, ReID.
Source attribution: curated from arXiv cs.AI (@Lyucheng Qian, John Yuehan Zhang, Pingyu Wang).