The technical contribution of this paper is limited, which is far from a decent ICLR paper. In particular, All kinds of evaluations, i.e., single-dataset setting (most of existing person re-ID methods), cross-dataset setting [1, 2,3] and live re-id setting [4], have been discussed in previous works. This paper simply makes a systematic discussion.
For cross-dataset setting, this paper only evaluates standard person re-ID methods that train on one dataset and evaluate on another, but fails to evaluate the typical cross-dataset person re-ID methods, e.g., [1, 2, 3].
For live re-ID setting, this paper does not compare with the particular live re-ID baseline [4]
Though some conclusions are drawn from the experiments, the novelty is limited. For example, 1) most of person re-ID methods build on the basis of pedestrian detector (two-step method), and there are also end-to-end method that combines detection and re-ID [5]; 2) It is common that distribution bias exists between datasets. It is hard to find a standard re-ID approach and a training dataset to address the problem unless the dataset is large enough that can cover as much as scenes. 3) cross-dataset methods try to mitigate the generalization problem.
[1] Hu, Yang, Dong Yi, Shengcai Liao, Zhen Lei, and Stan Z. Li. "Cross dataset person re-identification." In Asian Conference on Computer Vision, pp. 650-664. Springer, Cham, 2014.
[2] Lv, Jianming, Weihang Chen, Qing Li, and Can Yang. "Unsupervised cross-dataset person re-identification by transfer learning of spatial-temporal patterns." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7948-7956. 2018.
[3] Li, Yu-Jhe, Ci-Siang Lin, Yan-Bo Lin, and Yu-Chiang Frank Wang. "Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation." In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7919-7929. 2019.
[4] Sumari, Felix O., Luigy Machaca, Jose Huaman, Esteban WG Clua, and Joris Guérin. "Towards practical implementations of person re-identification from full video frames." Pattern Recognition Letters 138 (2020): 513-519.
[5] Xiao, Tong, Shuang Li, Bochao Wang, Liang Lin, and Xiaogang Wang. "Joint detection and identification feature learning for person search." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3415-3424. 2017.
1) most of person re-ID methods build on the basis of pedestrian detector (two-step method), and there are also end-to-end method that combines detection and re-ID [5];