Feb. 23, 2022, 5:05 p.m. | Synced

Synced syncedreview.com

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

More from syncedreview.com / Synced

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Data Engineer

@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania

Artificial Intelligence – Bioinformatic Expert

@ University of Texas Medical Branch | Galveston, TX

Lead Developer (AI)

@ Cere Network | San Francisco, US