Abstract
Occluded person re-identification (Re-ID) aims to address the potential occlusion problem when matching occluded or holistic pedestrians from different camera views. Many methods use the background as artificial occlusion and rely on attention networks to exclude noisy interference. However, the significant discrepancy between simple background occlusion and realistic occlusion can negatively impact the generalization of the this http URL address this issue, we propose a novel transformer-based Attention Disturbance and Dual-Path Constraint Network (ADP) to enhance the generalization of attention networks. Firstly, to imitate real-world obstacles, we introduce an Attention Disturbance Mask (ADM) module that generates an offensive noise, which can distract attention like a realistic occluder, as a more complex form of occlusion.Secondly, to fully exploit these complex occluded images, we develop a Dual-Path Constraint Module (DPC) that can obtain preferable supervision information from holistic images through dual-path interaction. With our proposed method, the network can effectively circumvent a wide variety of occlusions using the basic ViT baseline. Comprehensive experimental evaluations conducted on person re-ID benchmarks demonstrate the superiority of ADP over state-of-the-art methods.
Abstract (translated)
遮挡人重识别(Re-ID)的目标是在匹配遮挡或整体行人从不同相机视图中识别潜在 occlusion 问题。许多方法使用背景作为 artificial occlusion 并依靠注意力网络排除噪声干扰。然而,简单的背景 occlusion 和真实的 occlusion 之间的显著差异可能会负面影响 this http URL 的泛化性。为了解决这一问题,我们提出了一种新颖的Transformer-based注意力干扰和双重路径约束网络(ADP)来增强注意力网络的泛化性。首先,模仿现实世界障碍物,我们引入了注意力干扰掩码(ADM)模块,产生 offensive 噪声,可以像真实的 occlusion 一样分散注意力,成为一种更复杂的 occlusion 形式。其次, fully 利用了这些复杂的 occlusion 图像,我们开发了双重路径约束模块(DPC),可以通过双重路径交互从整体图像获取更好的监督信息。通过我们提出的方法,网络可以 effectively 通过基本 ViT 基线绕过各种 occlusion。在人重识别基准点的 comprehensive 实验评估表明,ADP 比现有方法优越。
URL
https://arxiv.org/abs/2303.10976
