Unsupervised learning is a challenging task due to the lack of labels.
Multiple Object Tracking (MOT), which inevitably suffers from mutual object
interference, occlusion, etc., is even more difficult without label
supervision. In this paper, we explore the latent consistency of sample
features across video frames and propose an Unsupervised Contrastive Similarity
Learning method, named UCSL, including three contrast modules: self-contrast,
cross-contrast, and ambiguity contrast. Specifically, i) self-contrast uses
intra-frame direct and inter-frame indirect contrast to obtain discriminative
representations by maximizing self-similarity. ii) Cross-contrast aligns cross-
and continuous-frame matching results, mitigating the persistent negative
effect caused by object occlusion. And iii) ambiguity contrast matches
ambiguous objects with each other to further increase the certainty of
subsequent object association through an implicit manner. On existing
benchmarks, our method outperforms the existing unsupervised methods using only
limited help from ReID head, and even provides higher accuracy than lots of
fully supervised methods.

本文探索了样本在视频帧之间的潜在一致性，并提出了一种无监督对比相似性学习方法 UCSL，其中包括自对比、交叉对比和模糊对比三个对比模块。我们的方法在现有基准上表现优于现有的无监督方法，并且甚至比许多完全监督方法提供更高的准确性。