Summary of Vig: Linear-complexity Visual Sequence Learning with Gated Linear Attention, by Bencheng Liao et al.
ViG: Linear-complexity Visual Sequence Learning with Gated Linear Attention
by Bencheng Liao, Xinggang Wang, Lianghui Zhu, Qian Zhang, Chang Huang
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel sequence modeling network, Gated Linear Attention (GLA), which leverages its superior hardware-awareness and efficiency to achieve better runtime speed. The GLA model, named ViG, uses direction-wise gating to capture 1D global context through bidirectional modeling and 2D gating locality injection to adaptively inject 2D local details into 1D global context. This approach enables ViG to offer a favorable trade-off in accuracy, parameters, and FLOPs on ImageNet and downstream tasks, outperforming popular Transformer and CNN-based models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new sequence modeling network that can do computer vision tasks really well while using less computing power. It’s called GLA (Gated Linear Attention) or ViG for short. The idea is to make the model better at using its hardware, like memory and processing power, so it runs faster and uses less energy. This helps the model be more efficient and scalable. |
Keywords
» Artificial intelligence » Attention » Cnn » Transformer
