Scale-invariant batch-adaptive residual learning for person re-identification

The problem of person re-identification (re-ID) deals with matching two similar persons in probe and gallery sets. The underlying pattern matching task can become more complex as similar persons can appear in different scales in the two sets. In this paper, we address this challenging problem of scale-invariant person re-ID. As a solution, we propose two scale-invariant residual networks with a new loss function for deep metric learning. The first network, termed as Scale Invariant Triplet Network (SI-TriNet), is deeper and is trained from the pre-trained weights. In contrast, the second network, named Scale-Invariant Siamese Resnet-32 (SISR-32), is shallower and uses training from the scratch. Deep metric learning for both the networks are realized through a batch adaptive triplet loss function. Extensive comparisons and ablation studies on the benchmark Market-1501 and CUHK03 datasets clearly demonstrate the effectiveness of the proposed formulation.