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Tsinghua & NKU’s Visual Attention Network Combines the Advantages of Convolution and Self-Attention, Achieves SOTA Performance on CV Tasks
Feb. 23, 2022, 5:05 p.m. | Synced
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In the new paper Visual Attention Network, a research team from Tsinghua University and Nankai University introduces a novel large kernel attention (LKA) mechanism for an extremely simple and efficient Visual Attention Network (VAN) that significantly outperforms state-of-the-art vision transformers and convolutional neural networks on various computer vision tasks.
The post Tsinghua & NKU’s Visual Attention Network Combines the Advantages of Convolution and Self-Attention, Achieves SOTA Performance on CV Tasks first appeared on Synced.
ai artificial intelligence attention computer vision & graphics cv machine learning machine learning & data science ml network neural networks performance research self-attention sota technology visual attention
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