In this paper, we propose an end-to-end hierarchical-based multi-mode attention network and adaptive fusion (HMAN-HAF) strategy to learn different-level salient features for re-ID tasks. First, according to each layer’s characteristics, a hierarchical multi-mode attention network (HMAN) is designed to adopt different attention models for different-level salient feature learning. Specifically, refined channel-wise attention (CA) is adopted to capture high-level valuable semantic information, an attentive region model (AR) is used to detect salient regions in the low layer, and fused attention (FA) is designed to capture the salient regions of valuable channels in the middle layer. Second, a hierarchical adaptive fusion (HAF) is constructed to fulfill the complementary strengths of different-level salient features. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on the following challenging benchmarks: Market-1501, DukeMTMC-reID and CUHK03.

在本文中，我们提出了一种基于端到端的基于层次的多模式注意力网络和自适应融合（HMAN-HAF）策略，以学习用于re-ID任务的不同级别的显着特征。首先，根据每一层的特征，设计了一种分层多模式注意力网络（HMAN），以采用不同的注意力模型进行不同级别的显着特征学习。具体而言，采用改进的按通道注意（CA）来捕获高级有价值的语义信息，使用注意区域模型（AR）来检测低层中的显着区域，并设计融合注意（FA）来捕获高级信息。中间层有价值通道的显着区域。其次，构建层次自适应融合（HAF）以实现不同级别显着特征的互补优势。