Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training
Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training
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 …