CAViT: Contextual Alignment Vision Transformer for Video Object Re-identification

Jinlin Wu, Lingxiao He, Wu Liu, Yang Yang, Zhen Lei, Tao Mei, Stan Z. Li ;

"Video object re-identification (reID) aims at re-identifying the same object under non-overlapping cameras by matching the video tracklets with cropped video frames. The key point is how to make full use of spatio-temporal interactions to extract more accurate representation. However, there are dilemmas within existing approaches: (1) 3D solutions model the spatio-temporal interaction but are often troubled with the misalignment of adjacent frames, and (2) 2D solutions adopt a divide-and-conquer strategy against the misalignment but cannot take advantage of the spatio-temporal interactions. To address the above problems, we propose a Contextual Alignment Vision Transformer (\textbf{CAViT}) to the spatio-temporal interaction with a 2D solution. It contains a Multi-shape Patch Embedding (\textbf{MPE}) module and a Temporal Shift Attention (\textbf{TSA}) module. MPE is designed to retain spatial semantic information against the misalignment caused by pose, occlusion, or misdetection. TSA is designed to achieve contextual spatial semantic feature alignment and jointly model spatio-temporal clues. We further propose a Residual Position Embedding (\textbf{RPE}) to guide TSA in focusing on the temporal saliency clues. Experimental results on five video person reID datasets demonstrate the superiority of the proposed CAViT. Additionally, the experiment conducted on VVeRI-901-trial also shows the effectiveness of CAViT for the video vehicle reID. Our code is available on \href{https://github.com/KimWu1994/CAViT}{https://github.com/KimWu1994/CAViT}."