Reviews: Deep Neural Networks with Inexact Matching for Person Re-Identification

The pros and Cons of the paper are as follows: the empirical results are strong, showing consistent advantage of the proposed method over previous art (as far as I can judge – I am not familiar with re-identification literature) the new normalized correlation layer is a sensible architecture for the re-identification task. Each input map is compared alone using normalized correlation to a large array of might-ne-relevant positions, providing reach output (1500 output neurons per location – that's a lot) for further processing. More detailed comments: Page 1: Lines 50-53: I did not understand the argument in these lines. It is stated that due to a large search area and inexact matching object parts in image 1 may be matched to background parts in image 2, and this is stated as a remedy to the partial occlusion problem. However, if this happens without significant penalty, irrelevant matches contribute to the score and may come to dominate it if there are many of them.