Sparse-attention augmented domain adaptation for unsupervised person re-identification

The domain gap persists as a demanding problem for unsupervised domain adaptive person re-identification (UDA re-ID). In response to this question, we present a novel Sparse self-Attention Augmented Domain Adaptation approach (SAADA Model) to promote network performance. In this work, we put forward a composite computational primitive (SAAP). The SAAP leverages sparse self-attention and convolution to enhance domain adaptation at the primitive level. Using SAAP as a core component, we construct an augmented bottleneck block to improve domain adaptation at the bottleneck block level. Finally, the augmented bottleneck block for domain adaptation can be cascaded into the SAADA module. After extensive experiments for UDA re-ID benchmarks, we deploy the SAADA module one time after the stage corresponding to the minimum feature map, and the performance of this method exceeds some SOTA methods. For example, the mAP has increased by 5.1% from the Market-1501 to the difficult MSMT17.