提出新方法让行人重识别在多域下表现更稳定，兼顾单域与跨域场景。
Diverse Deep Feature Ensemble Learning for Omni-Domain Generalized Person Re-identification
- 通过自集成生成多样深层特征，构建紧凑编码。
- 在主流跨域和单域基准上达到顶尖性能。
- 适合需要稳定跨域泛化能力的行人识别应用。
行人重识别（Person ReID）在单域监督设置下已接近性能饱和，但跨数据集测试时性能显著下降，推动了领域泛化技术的发展。然而我们发现，现有领域泛化方法在单数据集基准上仍远逊于单域监督方法。理想的重识别方法应无论涉及多少领域均有效；当测试域数据可用于训练时，其性能应媲美最先进的全监督方法。这一范式称为全域泛化行人重识别（ODG-ReID）。本文提出通过自集成生成深层特征多样性来实现ODG-ReID，方法名为D2FEL，采用独特的实例归一化模式生成多个多样化视图，并将这些视图重组为紧凑编码。据我们所知，这是少数关注行人重识别中全域泛化的工作之一，推进了特征集成在该领域的应用。D2FEL在主要跨域和单域监督基准上显著提升并达到最先进水平。
原文摘要 · Abstract (English)
Person Re-identification (Person ReID) has progressed to a level where single-domain supervised Person ReID performance has saturated. However, such methods experience a significant drop in performance when trained and tested across different datasets, motivating the development of domain generalization techniques. However, our research reveals that domain generalization methods significantly underperform single-domain supervised methods on single dataset benchmarks. An ideal Person ReID method should be effective regardless of the number of domains involved, and when test domain data is available for training it should perform as well as state-of-the-art (SOTA) fully supervised methods. This is a paradigm that we call Omni-Domain Generalization Person ReID (ODG-ReID). We propose a way to achieve ODG-ReID by creating deep feature diversity with self-ensembles. Our method, Diverse Deep Feature Ensemble Learning (D2FEL), deploys unique instance normalization patterns that generate multiple diverse views and recombines these views into a compact encoding. To the best of our knowledge, our work is one of few to consider omni-domain generalization in Person ReID, and we advance the study of applying feature ensembles in Person ReID. D2FEL significantly improves and matches the SOTA performance for major domain generalization and single-domain supervised benchmarks.
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