Fang, PengfeiZhou, JiemingRoy, Soumava KumarJi, PanPetersson, LarsHarandi, Mehrtash2023-12-070162-8828http://hdl.handle.net/1885/307725This paper generalizes the Attention in Attention (AiA) mechanism, proposed in [1], by employing explicit mapping in reproducing kernel Hilbert spaces to generate attention values of the input feature map. The AiA mechanism models the capacity of building inter-dependencies among the local and global features by the interaction of inner and outer attention modules. Besides a vanilla AiA module, termed linear attention with AiA, two non-linear counterparts, namely, second-order polynomial attention and Gaussian attention, are also proposed to utilize the non-linear properties of the input features explicitly, via the second-order polynomial kernel and Gaussian kernel approximation. The deep convolutional neural network, equipped with the proposed AiA blocks, is referred to as Attention in Attention Network (AiA-Net). The AiA-Net learns to extract a discriminative pedestrian representation, which combines complementary person appearance and corresponding part features. Extensive ablation studies verify the effectiveness of the AiA mechanism and the use of non-linear features hidden in the feature map for attention design. Furthermore, our approach outperforms current state-of-the-art by a considerable margin across a number of benchmarks. In addition, state-of-the-art performance is also achieved in the video person retrieval task with the assistance of the proposed AiA blocks.application/pdfen-AU© 2021 IEEEAttention in Attention MechanismPerson RetrievalPedestrian RepresentationConvolutional Neural NetworkSecond-order Polynomial KernelGaussian Kernel202110.1109/TPAMI.2021.30735122022-09-04