Strengthening Layer Interaction via Dynamic Layer Attention

• URL: http://arxiv.org/abs/2406.13392v1
• Date: Wed, 19 Jun 2024 09:35:14 GMT
• Title: Strengthening Layer Interaction via Dynamic Layer Attention
• Authors: Kaishen Wang, Xun Xia, Jian Liu, Zhang Yi, Tao He,
• Abstract summary: Existing layer attention methods achieve layer interaction on fixed feature maps in a static manner. To restore the dynamic context representation capability of the attention mechanism, we propose a Dynamic Layer Attention architecture. Experimental results demonstrate the effectiveness of the proposed DLA architecture, outperforming other state-of-the-art methods in image recognition and object detection tasks.
• Score: 12.341997220052486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, employing layer attention to enhance interaction among hierarchical layers has proven to be a significant advancement in building network structures. In this paper, we delve into the distinction between layer attention and the general attention mechanism, noting that existing layer attention methods achieve layer interaction on fixed feature maps in a static manner. These static layer attention methods limit the ability for context feature extraction among layers. To restore the dynamic context representation capability of the attention mechanism, we propose a Dynamic Layer Attention (DLA) architecture. The DLA comprises dual paths, where the forward path utilizes an improved recurrent neural network block, named Dynamic Sharing Unit (DSU), for context feature extraction. The backward path updates features using these shared context representations. Finally, the attention mechanism is applied to these dynamically refreshed feature maps among layers. Experimental results demonstrate the effectiveness of the proposed DLA architecture, outperforming other state-of-the-art methods in image recognition and object detection tasks. Additionally, the DSU block has been evaluated as an efficient plugin in the proposed DLA architecture.The code is available at https://github.com/tunantu/Dynamic-Layer-Attention.

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