Rethinking the Distribution Gap of Person Re-identification with Camera-based Batch Normalization
This addresses the costly annotation and limited transferability issues in ReID, offering a practical solution for real-world applications, though it is an incremental improvement over existing normalization techniques.
The paper tackles the problem of distribution gaps between cameras in person re-identification (ReID) by introducing Camera-based Batch Normalization (CBN), which aligns image data across cameras to improve generalization to unseen cameras and enable competitive performance using only intra-camera annotations.
The fundamental difficulty in person re-identification (ReID) lies in learning the correspondence among individual cameras. It strongly demands costly inter-camera annotations, yet the trained models are not guaranteed to transfer well to previously unseen cameras. These problems significantly limit the application of ReID. This paper rethinks the working mechanism of conventional ReID approaches and puts forward a new solution. With an effective operator named Camera-based Batch Normalization (CBN), we force the image data of all cameras to fall onto the same subspace, so that the distribution gap between any camera pair is largely shrunk. This alignment brings two benefits. First, the trained model enjoys better abilities to generalize across scenarios with unseen cameras as well as transfer across multiple training sets. Second, we can rely on intra-camera annotations, which have been undervalued before due to the lack of cross-camera information, to achieve competitive ReID performance. Experiments on a wide range of ReID tasks demonstrate the effectiveness of our approach. The code is available at https://github.com/automan000/Camera-based-Person-ReID.