ARTransformer: An Architecture of Resolution Representation Learning for Cross-Resolution Person Re-Identification

Abstract

Cross-resolution person re-identification (CR-ReID) seeks to overcome the challenge of retrieving and matching specific person images across cameras with varying resolutions. Numerous existing studies utilize established CNNs and ViTs to resize captured low-resolution (LR) images and align them with high-resolution (HR) image features or construct common feature spaces to match between images of different resolutions. However, these methods ignore the potential feature connection between the LR and HR images of the same pedestrian identity. Besides, the CNNs or ViTs usually obtain outliers within the attention maps of LR images; this inclination to excessively concentrate on anomalous information may obscure the genuine and anticipated characteristics between images, which makes it challenging to extract meaningful information from the images. In this work, we propose the abnormal feature elimination and reconfiguration Transformer (ARTransformer), a novel network architecture for robust cross-resolution person re-identification tasks. This method uses a resolution feature discriminator to learn resolution-invariant features and output feature matrices of images with different resolutions. It then calculates the potential feature relationships between images of pedestrians with the same identity but different resolutions through a new cross-resolution landmark agent attention (CR-LAA) mechanism. Conclusively, it utilizes output feature matrices to model LR and HR image interactions by mitigating abnormal image features and prioritizing attention on the target person by learning representations from input images of various resolutions. Experimental results show that ARTransformer performs well in matching images with different resolutions, even with unseen resolution, and extensive evaluations on four real-world datasets confirm the excellent results of our approach.