Our impression about one person often updates after we see more aspects of him/her and this process keeps iterating given more meetings. We formulate such an intuition into the problem of person re-identification (re-ID), where the representation of a query (probe) image is iteratively updated with new information from the candidates in the gallery. Specifically, we propose a simple attentional aggregation formulation to instantiate this idea and showcase that such a pipeline achieves competitive performance on standard benchmarks including CUHK03, Market-1501 and DukeMTMC. Not only does such a simple method improve the performance of the baseline models, it also achieves comparable performance with latest advanced re-ranking methods. Another advantage of this proposal is its flexibility to incorporate different representations and similarity metrics. By utilizing stronger representations and metrics, we further demonstrate state-of-the-art person re-ID performance, which also validates the general applicability of the proposed method.
@article{arxiv.2009.10066,
title = {Improving Person Re-identification with Iterative Impression Aggregation},
author = {Dengpan Fu and Bo Xin and Jingdong Wang and Dongdong Chen and Jianmin Bao and Gang Hua and Houqiang Li},
journal= {arXiv preprint arXiv:2009.10066},
year = {2020}
}