Abstract
Occluded person re-identification aims to retrieve holistic images based on occluded ones. Existing methods often rely on aligning visible body parts, applying occlusion augmentation, or complementing missing semantics using holistic images. However, they face challenges in handling diverse occlusion scenarios not seen during training and the issue of feature contamination from holistic images. To address these limitations, we propose Occlusion-Guided Feature Purification Learning via Reinforced Knowledge Distillation (OGFR), which simultaneously mitigates these challenges. OGFR adopts a teacher-student distillation architecture that effectively incorporates diverse occlusion patterns into feature representation while transferring the purified discriminative holistic knowledge from the holistic to the occluded branch through reinforced knowledge distillation. Specifically, an Occlusion-Aware Vision Transformer is designed to leverage learnable occlusion pattern embeddings to explicitly model such diverse occlusion types, thereby guiding occlusion-aware robust feature representation. Moreover, we devise a Feature Erasing and Purification Module within the holistic branch, in which an agent is employed to identify low-quality patch tokens of holistic images that contain noisy negative information via deep reinforcement learning, and substitute these patch tokens with learnable embedding tokens to avoid feature contamination and further excavate identity-related discriminative clues. Afterward, with the assistance of knowledge distillation, the student branch effectively absorbs the purified holistic knowledge to precisely learn robust representation regardless of the interference of occlusions.
Abstract (translated)
遮挡人物重识别的目标是根据带有遮挡的图像检索出完整的图像。现有的方法通常依赖于对可见身体部位进行对齐，应用遮挡增强处理，或使用完整图象来补充缺失的语义信息。然而，这些方法在面对训练期间未见过的各种遮挡场景和来自完整图像特征污染的问题时面临挑战。 为了克服这些限制，我们提出了通过强化知识蒸馏实现的遮挡引导特征净化学习（OGFR），这种方法同时缓解了上述挑战。OGFR采用了一种教师-学生蒸馏架构，该架构能够有效地将各种遮挡模式整合进特征表示中，并通过强化知识蒸馏从完整分支向遮挡分支传输经过净化的鉴别性完整图像知识。 具体而言，设计了一个感知遮挡的视觉变换器（Occlusion-Aware Vision Transformer），利用可学习的遮挡模式嵌入来显式建模各种多样的遮挡类型，从而引导生成针对遮挡敏感且鲁棒的特征表示。此外，在完整的分支中，我们开发了一个特征擦除和净化模块，其中使用代理通过深度强化学习识别包含嘈杂负信息的完整图像低质量补丁标记，并用可学习嵌入标记替换这些补丁标记以避免特征污染并进一步挖掘与身份相关的鉴别性线索。 最后，在知识蒸馏的帮助下，学生分支能够有效吸收经过净化的完整图像知识，从而无论遮挡干扰如何都能精确地学习到鲁棒表示。
URL
https://arxiv.org/abs/2507.08520
