用于遮挡行人重识别的动态块感知增强Transformer
Dynamic Patch-aware Enrichment Transformer for Occluded Person Re-Identification
AI总结：
提出DPEFormer，通过动态块token选择模块和特征混合模块自动区分人体与遮挡，并结合SAM的真实遮挡增强策略，在遮挡行人重识别任务上取得显著提升。
摘要：
行人重识别（re-ID）仍然是一个重大挑战，特别是在涉及遮挡的场景中。以往旨在解决遮挡问题的方法主要侧重于通过利用外部语义线索来对齐物理身体特征。然而，这些方法往往较为复杂且容易受到噪声影响。为了解决上述挑战，我们提出了一种创新的端到端解决方案，称为动态块感知增强Transformer（DPEFormer）。该模型能够自动且动态地将人体信息与遮挡区分开来，无需外部检测器或精确的图像对齐。具体而言，我们引入了一个动态块token选择模块（DPSM）。DPSM利用一个标签引导的代理token作为中介，来识别信息丰富的无遮挡token。随后选择这些token用于推导后续的局部部位特征。为了促进全局分类特征与DPSM所选的精细局部特征的无缝集成，我们引入了一个新颖的特征混合模块（FBM）。FBM通过信息的互补性和部位多样性的利用来增强特征表示。此外，为了确保DPSM和整个DPEFormer能够仅使用身份标签进行有效学习，我们还提出了一种真实遮挡增强（ROA）策略。该策略利用了Segment Anything Model（SAM）的最新进展。因此，它生成的遮挡图像与真实世界的遮挡非常相似，极大地增强了后续的对比学习过程。在遮挡和整体re-ID基准上的实验表明，DPEFormer相对于现有的最先进方法取得了显著进步。代码将公开发布。
英文摘要：
Person re-identification (re-ID) continues to pose a significant challenge, particularly in scenarios involving occlusions. Prior approaches aimed at tackling occlusions have predominantly focused on aligning physical body features through the utilization of external semantic cues. However, these methods tend to be intricate and susceptible to noise. To address the aforementioned challenges, we present an innovative end-to-end solution known as the Dynamic Patch-aware Enrichment Transformer (DPEFormer). This model effectively distinguishes human body information from occlusions automatically and dynamically, eliminating the need for external detectors or precise image alignment. Specifically, we introduce a dynamic patch token selection module (DPSM). DPSM utilizes a label-guided proxy token as an intermediary to identify informative occlusion-free tokens. These tokens are then selected for deriving subsequent local part features. To facilitate the seamless integration of global classification features with the finely detailed local features selected by DPSM, we introduce a novel feature blending module (FBM). FBM enhances feature representation through the complementary nature of information and the exploitation of part diversity. Furthermore, to ensure that DPSM and the entire DPEFormer can effectively learn with only identity labels, we also propose a Realistic Occlusion Augmentation (ROA) strategy. This strategy leverages the recent advances in the Segment Anything Model (SAM). As a result, it generates occlusion images that closely resemble real-world occlusions, greatly enhancing the subsequent contrastive learning process. Experiments on occluded and holistic re-ID benchmarks signify a substantial advancement of DPEFormer over existing state-of-the-art approaches. The code will be made publicly available.
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。
