Summary of Duoformer: Leveraging Hierarchical Visual Representations by Local and Global Attention, By Xiaoya Tang et al.
DuoFormer: Leveraging Hierarchical Visual Representations by Local and Global Attention
by Xiaoya Tang, Bodong Zhang, Beatrice S. Knudsen, Tolga Tasdizen
First submitted to arxiv on: 18 Jul 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 A novel hierarchical transformer model, dubbed DuoFormer, is proposed to combine the strengths of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). The model addresses limitations in ViTs by using a CNN backbone for feature extraction, followed by patch tokenization to adapt visual representations for transformer input. Additionally, a ‘scale attention’ mechanism is introduced to capture cross-scale dependencies, enhancing spatial understanding while preserving global perception. Experimental results demonstrate DuoFormer’s efficiency and generalizability, outperforming baseline models on small and medium-sized medical datasets. The model’s components are designed as plug-and-play with different CNN architectures and can be adapted for various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We propose a new AI model that helps computers understand pictures better. This model combines two previous approaches: Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). We want our model to work well even when it only has a little training data, which is a problem with some current models. To make this happen, we use the CNN part to extract important features from pictures, and then adapt those features for the ViT part to understand. We also add a special attention mechanism that helps our model see patterns across different parts of the picture. Our new model works well on medical datasets and can be used in many other areas. |
Keywords
» Artificial intelligence » Attention » Cnn » Feature extraction » Tokenization » Transformer » Vit
