基于强化知识蒸馏的遮挡引导特征净化学习用于遮挡行人重识别
Occlusion-Guided Feature Purification Learning via Reinforced Knowledge Distillation for Occluded Person Re-Identification
AI总结：
提出OGFR方法，通过师生蒸馏架构和遮挡感知视觉Transformer建模多样遮挡模式，并利用强化学习驱动的特征擦除与净化模块去除整体图像中的噪声patch tokens，将净化后的判别性知识传递给遮挡分支，有效解决未见遮挡场景和特征污染问题，实现鲁棒的遮挡行人重识别。
摘要：
遮挡行人重识别旨在基于遮挡图像检索整体图像。现有方法通常依赖对齐可见身体部位、应用遮挡增强，或使用整体图像补充缺失语义。然而，它们在处理训练中未见过的多样遮挡场景以及来自整体图像的特征污染问题时面临挑战。为解决这些局限性，我们提出基于强化知识蒸馏的遮挡引导特征净化学习（OGFR），同时缓解这些挑战。OGFR采用师生蒸馏架构，有效将多样遮挡模式融入特征表示，同时通过强化知识蒸馏将净化的判别性整体知识从整体分支转移到遮挡分支。具体而言，设计了遮挡感知视觉Transformer，利用可学习的遮挡模式嵌入显式建模此类多样遮挡类型，从而引导遮挡感知的鲁棒特征表示。此外，我们在整体分支内设计了特征擦除与净化模块，其中采用智能体通过深度强化学习识别包含噪声负面信息的整体图像低质量patch tokens，并用可学习的嵌入tokens替换这些patch tokens，以避免特征污染并进一步挖掘与身份相关的判别线索。随后，在知识蒸馏的辅助下，学生分支有效吸收净化的整体知识，无论遮挡干扰如何，都能精确学习鲁棒表示。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
