  Weakly supervised person search aims to jointly detect and match persons with
only bounding box annotations. Existing approaches typically focus on improving
the features by exploring relations of persons. However, scale variation
problem is a more severe obstacle and under-studied that a person often owns
images with different scales (resolutions). On the one hand, small-scale images
contain less information of a person, thus affecting the accuracy of the
generated pseudo labels. On the other hand, the similarity of cross-scale
images is often smaller than that of images with the same scale for a person,
which will increase the difficulty of matching. In this paper, we address this
problem by proposing a novel one-step framework, named Self-similarity driven
Scale-invariant Learning (SSL). Scale invariance can be explored based on the
self-similarity prior that it shows the same statistical properties of an image
at different scales. To this end, we introduce a Multi-scale Exemplar Branch to
guide the network in concentrating on the foreground and learning
scale-invariant features by hard exemplars mining. To enhance the
discriminative power of the features in an unsupervised manner, we introduce a
dynamic multi-label prediction which progressively seeks true labels for
training. It is adaptable to different types of unlabeled data and serves as a
compensation for clustering based strategy. Experiments on PRW and CUHK-SYSU
databases demonstrate the effectiveness of our method.
