QEA-Net: Quantum-Effects-based Attention Networks
PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT III(2024)
摘要
In the past decade, the attention mechanism has played an increasingly important role in computer vision. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. In this paper, we propose Quantum-Effects-based Attention Networks (QEA-Net), the simple yet effective attention networks, they can be integrated into many network architectures seamlessly. QEA-Net uses quantum effects between two identical particles to enhance the global channel information representation of the attention module. Our method could consistently outperform the SENet, with a lower number of parameters and computational cost. We evaluate QEA-Net through experiments on ImageNet-1K and compare it with state-of-the-art counterparts. We also demonstrated the effect of QEA-Net in combination with pre-trained networks on small downstream transfer learning tasks.
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关键词
Quantum mechanics,Attention mechanism,Image classification
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