Summary of Embanet: a Flexible Efffcient Multi-branch Attention Network, by Keke Zu and Hu Zhang and Jian Lu and Lei Zhang and Chen Xu
EMBANet: A Flexible Efffcient Multi-branch Attention Network
by Keke Zu, Hu Zhang, Jian Lu, Lei Zhang, Chen Xu
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a novel module called Multi-Branch Concat (MBC) for processing input tensors and obtaining multi-scale feature maps. The MBC module introduces new degrees of freedom for attention network design by allowing flexible adjustment of transformation operators and branch numbers. Two transformation operators, multiplex and split, are considered, which can represent multi-scale features at a granular level and increase receptive fields. By combining MBC with attention modules, the Multi-Branch Attention (MBA) module is developed to capture channel-wise interactions for establishing long-range dependencies. The proposed Efficient Multi-Branch Attention (EMBA) block substitutes 3×3 convolutions in ResNet bottleneck blocks, which can be easily integrated into state-of-the-art CNN backbones. Additionally, a new backbone network called EMBANet is established by stacking EMBA blocks and evaluated on computer vision tasks such as classification, detection, and segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to help computers understand images better. It’s like adding extra tools to a camera that can take pictures of different parts of the image at the same time. This helps the camera learn more about what it’s seeing and make better decisions. The new tool is called MBC, which stands for Multi-Branch Concat. It lets computer scientists design attention networks in a more flexible way. Two special tools are used to help the camera understand different parts of the image: multiplex and split. These tools can take pictures of small or big parts of the image at the same time, making it easier to learn about what’s happening in the picture. |
Keywords
» Artificial intelligence » Attention » Classification » Cnn » Resnet
