Despite the recent success of deep learning architectures, person re-identification (ReID) remains a challenging problem in real-word applications. Several unsupervised single-target domain adaptation (STDA) methods have recently been proposed to limit the decline in ReID accuracy caused by the domain shift that typically occurs between source and target video data. Given the multimodal nature of person ReID data (due to variations across camera viewpoints and capture conditions), training a common CNN backbone to address domain shifts across multiple target domains, can provide an efficient solution for real-time ReID applications. Although multi-target domain adaptation (MTDA) has not been widely addressed in the ReID literature, a straightforward approach consists in blending different target datasets, and performing STDA on the mixture to train a common CNN. However, this approach may lead to poor generalization, especially when blending a growing number of distinct target domains to train a smaller CNN. To alleviate this problem, we introduce a new MTDA method based on knowledge distillation (KD-ReID) that is suitable for real-time person ReID applications. Our method adapts a common lightweight student backbone CNN over the target domains by alternatively distilling from multiple specialized teacher CNNs, each one adapted on data from a specific target domain. Extensive experiments conducted on several challenging person ReID datasets indicate that our approach outperforms state-of-art methods for MTDA, including blending methods, particularly when training a compact CNN backbone like OSNet. Results suggest that our flexible MTDA approach can be employed to design cost-effective ReID systems for real-time video surveillance applications.
翻译:尽管最近深层学习架构取得了成功,但个人再识别(ReID)仍然是现实应用中一个具有挑战性的问题。最近提出了几种未经监督的单一目标域适应(STDA)方法,以限制源与目标视频数据之间通常发生的域变换导致的ReID准确性下降。鉴于人再识别数据多式性质(由于镜头视角和捕获条件的差异),培训一个共同的CNN骨干,以应对跨多个目标域的域变,可以为实时ReID应用程序提供一个有效的解决方案。虽然ReID文献尚未广泛处理多目标域变换(MTDA)问题,但一种直截了当的方法是混合不同的目标数据集(STDA),在混合不同目标数据集上执行STA。然而,这一方法可能导致不甚全面化,特别是在将越来越多的不同的目标域内培训领域结合起来时,我们引入一种新的MTDA方法,即适合实时人再识别应用程序(KD-ReID),我们的方法是将一个共同的轻度的SDRISDMS监控网格方法从一个特定的域域域内测试方法调整。