论文标题
通过迭代印象聚集来改善人员重新识别
Improving Person Re-identification with Iterative Impression Aggregation
论文作者
论文摘要
在我们看到他/她的更多方面之后，我们对一个人的印象经常会更新，并且在更多的会议上，这个过程不断迭代。我们将这种直觉提出了重新识别问题（RE-ID）的直觉，其中查询（探针）图像的表示形式迭代地使用了画廊中候选人的新信息。具体而言，我们提出了一种简单的注意聚合配方，以实例化这一想法并展示了这样的管道在包括Cuhk03，Market-1501和Dukemtmc在内的标准基准上实现竞争性能。这种简单的方法不仅可以改善基线模型的性能，还可以通过最新的高级重新排列方法实现可比性的性能。该提案的另一个优点是它的灵活性可以合并不同的表示和相似性指标。通过利用更强大的表示和指标，我们进一步证明了最先进的人重新ID绩效，这也验证了所提出方法的一般适用性。
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.
