Explicitly Modeled Attention Maps for Image Classification

• URL: http://arxiv.org/abs/2006.07872v2
• Date: Thu, 18 Mar 2021 14:18:57 GMT
• Title: Explicitly Modeled Attention Maps for Image Classification
• Authors: Andong Tan, Duc Tam Nguyen, Maximilian Dax, Matthias Nie{\ss}ner, Thomas Brox
• Abstract summary: Self-attention networks have shown remarkable progress in computer vision tasks such as image classification. We propose a novel self-attention module with explicitly modeled attention-maps using only a single learnable parameter for low computational overhead. Our method achieves an accuracy improvement of up to 2.2% over the ResNet-baselines in ImageNet ILSVRC.
• Score: 35.72763148637619
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-attention networks have shown remarkable progress in computer vision tasks such as image classification. The main benefit of the self-attention mechanism is the ability to capture long-range feature interactions in attention-maps. However, the computation of attention-maps requires a learnable key, query, and positional encoding, whose usage is often not intuitive and computationally expensive. To mitigate this problem, we propose a novel self-attention module with explicitly modeled attention-maps using only a single learnable parameter for low computational overhead. The design of explicitly modeled attention-maps using geometric prior is based on the observation that the spatial context for a given pixel within an image is mostly dominated by its neighbors, while more distant pixels have a minor contribution. Concretely, the attention-maps are parametrized via simple functions (e.g., Gaussian kernel) with a learnable radius, which is modeled independently of the input content. Our evaluation shows that our method achieves an accuracy improvement of up to 2.2% over the ResNet-baselines in ImageNet ILSVRC and outperforms other self-attention methods such as AA-ResNet152 in accuracy by 0.9% with 6.4% fewer parameters and 6.7% fewer GFLOPs. This result empirically indicates the value of incorporating geometric prior into self-attention mechanism when applied in image classification.

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