Omni-Scale Feature Learning for Person Re-Identification

Understand

As an instance-level recognition problem, person re-identification (ReID) relies on discriminative features, which not only capture different spatial scales but also encapsulate an arbitrary combination of multiple scales.

• We call features of both homogeneous and heterogeneous scales omni-scale features.
• In this paper, a novel deep ReID CNN is designed, termed Omni-Scale Network (OSNet), for omni-scale feature learning.
• This is achieved by designing a residual block composed of multiple convolutional streams, each detecting features at a certain scale.