Video Temporal Relationship Mining for Data-Efficient Person Re-identification
This addresses data-efficient person re-identification for surveillance and security applications, but it is incremental as it builds on existing retrieval methods.
The paper tackles person re-identification by treating query and gallery images as video frames and proposes a post-processing strategy that iteratively retrieves similar images to improve accuracy, achieving competitive results in the ICCV 2021 VIPriors challenge.
This paper is a technical report to our submission to the ICCV 2021 VIPriors Re-identification Challenge. In order to make full use of the visual inductive priors of the data, we treat the query and gallery images of the same identity as continuous frames in a video sequence. And we propose one novel post-processing strategy for video temporal relationship mining, which not only calculates the distance matrix between query and gallery images, but also the matrix between gallery images. The initial query image is used to retrieve the most similar image from the gallery, then the retrieved image is treated as a new query to retrieve its most similar image from the gallery. By iteratively searching for the closest image, we can achieve accurate image retrieval and finally obtain a robust retrieval sequence.