Abstract
Existing person re-identification methods often suffer significant performance degradation on unseen domains, which fuels interest in domain generalizable person re-identification (DG-PReID). As an effective technology to alleviate domain variance, the Instance Normalization (IN) has been widely employed in many existing works. However, IN also suffers from the limitation of eliminating discriminative patterns that might be useful for a particular domain or instance. In this work, we propose a new normalization scheme called Dynamically Transformed Instance Normalization (DTIN) to alleviate the drawback of IN. Our idea is to employ dynamic convolution to allow the unnormalized feature to control the transformation of the normalized features into new representations. In this way, we can ensure the network has sufficient flexibility to strike the right balance between eliminating irrelevant domain-specific features and adapting to individual domains or instances. We further utilize a multi-task learning strategy to train the model, ensuring it can adaptively produce discriminative feature representations for an arbitrary domain. Our results show a great domain generalization capability and achieve state-of-the-art performance on three mainstream DG-PReID settings.
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Acknowledgments
This work is supported by National Key R &D Program of China (No.2020AAA0106900), the National Natural Science Foundation of China (No. U19B2037), Shaanxi Provincial Key R &D Program (No.2021KWZ–03), Natural Science Basic Research Program of Shaanxi (No.2021JCW–03). This work is also supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG–RP–2018–003), and the MOE AcRF Tier-1 research grant: RG95/20.
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Jiao, B. et al. (2022). Dynamically Transformed Instance Normalization Network for Generalizable Person Re-Identification. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13674. Springer, Cham. https://doi.org/10.1007/978-3-031-19781-9_17
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