Learning Generalisable Omni-Scale Representations for Person Re-Identification
This work addresses the challenge of learning generalizable and discriminative features for person re-identification, which is important for surveillance and security applications, and it is incremental as it builds on existing CNN methods with novel architectural components.
The paper tackles the problem of person re-identification by developing a lightweight CNN architecture called OSNet that learns omni-scale features and incorporates instance normalization for cross-dataset generalization, achieving state-of-the-art performance in same-dataset settings and outperforming most unsupervised domain adaptation methods in cross-dataset settings without using target data.
An effective person re-identification (re-ID) model should learn feature representations that are both discriminative, for distinguishing similar-looking people, and generalisable, for deployment across datasets without any adaptation. In this paper, we develop novel CNN architectures to address both challenges. First, we present a re-ID CNN termed omni-scale network (OSNet) to learn features that not only capture different spatial scales but also encapsulate a synergistic combination of multiple scales, namely omni-scale features. The basic building block consists of multiple convolutional streams, each detecting features at a certain scale. For omni-scale feature learning, a unified aggregation gate is introduced to dynamically fuse multi-scale features with channel-wise weights. OSNet is lightweight as its building blocks comprise factorised convolutions. Second, to improve generalisable feature learning, we introduce instance normalisation (IN) layers into OSNet to cope with cross-dataset discrepancies. Further, to determine the optimal placements of these IN layers in the architecture, we formulate an efficient differentiable architecture search algorithm. Extensive experiments show that, in the conventional same-dataset setting, OSNet achieves state-of-the-art performance, despite being much smaller than existing re-ID models. In the more challenging yet practical cross-dataset setting, OSNet beats most recent unsupervised domain adaptation methods without using any target data. Our code and models are released at \texttt{https://github.com/KaiyangZhou/deep-person-reid}.