针对手绘素描与监控图像匹配难题，提出理论指导的少样本跨模态方法。
A Theory-Inspired Framework for Few-Shot Cross-Modal Sketch Person Re-Identification
- 基于泛化理论设计对齐增强与知识催化模块
- 在数据稀缺下仍实现领先性能，显著提升跨模态匹配准确率
- 适合少样本、跨模态识别场景，尤其适用于素描检索任务
基于素描的人体再识别旨在匹配手绘素描与RGB监控图像，但因模态差异大且标注数据有限而极具挑战。为此，我们提出KTCAA——一种理论驱动的少样本跨模态泛化框架。受泛化理论启发，我们识别出影响目标域风险的两个关键因素：(1) 域差异，衡量源域与目标域分布对齐的难度；(2) 扰动不变性，评估模型对模态变化的鲁棒性。据此，我们设计两个组件：(1) 对齐增强（AA），通过局部素描风格变换模拟目标分布，促进渐进式对齐；(2) 知识迁移催化剂（KTC），引入最坏情况扰动并强制一致性以增强不变性。两者在元学习框架下联合优化，实现从数据丰富RGB域向素描场景的对齐知识迁移。多基准测试表明，KTCAA在数据稀缺条件下达到最新性能，显著优于现有方法。
原文摘要 · Abstract (English)
Sketch based person re-identification aims to match hand-drawn sketches with RGB surveillance images, but remains challenging due to significant modality gaps and limited annotated data. To address this, we introduce KTCAA, a theoretically grounded framework for few-shot cross-modal generalization. Motivated by generalization theory, we identify two key factors influencing target domain risk: (1) domain discrepancy, which quantifies the alignment difficulty between source and target distributions; and (2) perturbation invariance, which evaluates the model's robustness to modality shifts. Based on these insights, we propose two components: (1) Alignment Augmentation (AA), which applies localized sketch-style transformations to simulate target distributions and facilitate progressive alignment; and (2) Knowledge Transfer Catalyst (KTC), which enhances invariance by introducing worst-case perturbations and enforcing consistency. These modules are jointly optimized under a meta-learning paradigm that transfers alignment knowledge from data-rich RGB domains to sketch-based scenarios. Experiments on multiple benchmarks demonstrate that KTCAA achieves state-of-the-art performance, particularly in data-scarce conditions.
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