TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification: Appendix

For ease and reliable comparison, we report the average of all Rank-1 and mAP results on all test datasets over several random runs for ablation study and parameter analysis. This is denoted by mAcc. There are three reasons that we use mAcc. It is a unified measure, which is convenient for algorithm comparison. Both Rank-1 and mAP are accuracy measures ranging from 0%-100%, thus averaging them is possible. Besides, if a method's mAcc is 1% higher than another method, on average it means that every single measure on each dataset has been increased by 1%, which is a perceptible achievement.