用于弱监督单步人员搜索的深度图像内对比学习
Deep Intra-Image Contrastive Learning for Weakly Supervised One-Step Person Search
AI总结：
本文提出基于Siamese network的深度图像内对比学习方法，通过空间不变对比和遮挡不变对比模块解决空间级与遮挡级方差问题，在CUHK-SYSU和PRW数据集上实现了弱监督单步人员搜索的最先进性能。
摘要：
弱监督人员搜索旨在仅使用行人边界框标注来完成行人检测和重识别（re-id）的联合任务。近期，对比学习的思想被初步应用于弱监督人员搜索，其中两种常见的对比策略是基于记忆的对比和图像内对比。我们认为当前的图像内对比是浅层的，存在空间级和遮挡级的方差问题。本文提出了一种使用Siamese network的新型深度图像内对比学习方法。两个关键模块是空间不变对比（SIC）和遮挡不变对比（OIC）。SIC在Siamese network的两个分支之间执行多对一对比，并在其中一个分支内执行密集预测对比。通过这些多对一和密集对比，SIC倾向于学习具有判别性的尺度不变和位置不变特征，以解决空间级方差问题。OIC利用掩码策略增强特征一致性，以学习遮挡不变特征。我们在两个人员搜索数据集CUHK-SYSU和PRW上分别进行了大量实验。我们的方法在弱监督单步人员搜索方法中实现了最先进的性能。我们希望简单的图像内对比学习能为弱监督人员搜索提供更多范式。源代码可在https://github.com/jiabeiwangTJU/DICL获取。
英文摘要：
Weakly supervised person search aims to perform joint pedestrian detection and re-identification (re-id) with only person bounding-box annotations. Recently, the idea of contrastive learning is initially applied to weakly supervised person search, where two common contrast strategies are memory-based contrast and intra-image contrast. We argue that current intra-image contrast is shallow, which suffers from spatial-level and occlusion-level variance. In this paper, we present a novel deep intra-image contrastive learning using a Siamese network. Two key modules are spatial-invariant contrast (SIC) and occlusion-invariant contrast (OIC). SIC performs many-to-one contrasts between two branches of Siamese network and dense prediction contrasts in one branch of Siamese network. With these many-to-one and dense contrasts, SIC tends to learn discriminative scale-invariant and location-invariant features to solve spatial-level variance. OIC enhances feature consistency with the masking strategy to learn occlusion-invariant features. Extensive experiments are performed on two person search datasets CUHK-SYSU and PRW, respectively. Our method achieves a state-of-the-art performance among weakly supervised one-step person search approaches. We hope that our simple intra-image contrastive learning can provide more paradigms on weakly supervised person search. The source code is available at \url{https://github.com/jiabeiwangTJU/DICL}.
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。
