Cross-layer Attention Network for Fine-grained Visual Categorization

• URL: http://arxiv.org/abs/2210.08784v1
• Date: Mon, 17 Oct 2022 06:57:51 GMT
• Title: Cross-layer Attention Network for Fine-grained Visual Categorization
• Authors: Ranran Huang, Yu Wang, Huazhong Yang
• Abstract summary: Learning discnative representations for subtle localized details plays a significant role in Fine-grained Visual Categorization (FGVC) We build a mutual refinement mechanism between the mid-level feature maps and the top-level feature map by our proposed Cross-layer Attention Network (CLAN) Experimental results show our approach achieves state-of-the-art on three publicly available fine-grained recognition datasets.
• Score: 12.249254142531381
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning discriminative representations for subtle localized details plays a significant role in Fine-grained Visual Categorization (FGVC). Compared to previous attention-based works, our work does not explicitly define or localize the part regions of interest; instead, we leverage the complementary properties of different stages of the network, and build a mutual refinement mechanism between the mid-level feature maps and the top-level feature map by our proposed Cross-layer Attention Network (CLAN). Specifically, CLAN is composed of 1) the Cross-layer Context Attention (CLCA) module, which enhances the global context information in the intermediate feature maps with the help of the top-level feature map, thereby improving the expressive power of the middle layers, and 2) the Cross-layer Spatial Attention (CLSA) module, which takes advantage of the local attention in the mid-level feature maps to boost the feature extraction of local regions at the top-level feature maps. Experimental results show our approach achieves state-of-the-art on three publicly available fine-grained recognition datasets (CUB-200-2011, Stanford Cars and FGVC-Aircraft). Ablation studies and visualizations are provided to understand our approach. Experimental results show our approach achieves state-of-the-art on three publicly available fine-grained recognition datasets (CUB-200-2011, Stanford Cars and FGVC-Aircraft).

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