Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training

Understand

Most existing Re-IDentification (Re-ID) methods are highly dependent on precise bounding boxes that enable images to be aligned with each other.

• However, due to the challenging practical scenarios, current detection models often produce inaccurate bounding boxes, which inevitably degenerate the performance of existing Re-ID algorithms.
• In this paper, we propose a novel coarse-to-fine pyramid model to relax the need of bounding boxes, which not only incorporates local and global information, but also integrates the gradual cues between them.
• The pyramid model is able to match at different scales and then search for the correct image of the same identity, even when the image pairs are not aligned.