Spatial and Temporal Dual-Attention for Unsupervised Person Re-Identification

Unsupervised person re-identification (Re-ID) aims to learn discriminative representations for person retrieval from unlabeled data. Recent research accomplishes this task with pseudo-labels and a center-level memory, but the pseudo-labels are inherently noisy and the update status of central features in memory is inconsistent, thus reducing the accuracy of Re-ID. In this paper, we propose a novel Spatial and Temporal Dual-Attention (STDA) framework to solve the above two problems. Firstly, to overcome the noisy label problem, we design an Intra-class Neighbor-based Spatial Attention (INSA) module to refine pseudo-labels by mining the spatial-level connections of positive instances. Specifically, we design a neighbor agreement as the similarity between central features and positive instance features in feature space to exploit the reliable complementary relationship. Based on the neighbor agreement, we aggregate the predictions of positive instances, thus jointly mitigating the noise in single instance feature clustering. Secondly, the central features in memory cannot be updated simultaneously, which leads to an inter-class update inconsistency problem. We introduce an Inter-class Sequence-based Temporal Attention (ISTA) module to alleviate this problem by computing a memory update factor based on the temporal-level update sequence of centrals. The weights are larger for newer updated centrals than for older updated centrals and non-updated centrals to mitigate the inter-class inconsistent update status. Finally, we combine the INSA and ISTA modules with contrastive learning for training. Extensive experimental results on Market-1501, MSMT17, and VeRi-776 show the effectiveness of the proposed method over the state-of-the-art performance. The code is available at:
https://github.com/heqlin5/STDA
.