Omni-Scale Feature Learning for Person Re-Identification

As an instance-level recognition problem, person reidentification (re-ID) 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 re-ID 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. Importantly, a novel unified aggregation gate is introduced to dynamically fuse multi-scale features with input-dependent channel-wise weights. To efficiently learn spatial-channel correlations and avoid overfitting, the building block uses pointwise and depthwise convolutions. By stacking such block layerby-layer, our OSNet is extremely lightweight and can be trained from scratch on existing re-ID benchmarks. Despite its small model size, OSNet achieves state-of-the-art performance on six person re-ID datasets, outperforming most large-sized models, often by a clear margin. Code and models are available at: https://github.com/ KaiyangZhou/deep-person-reid .