Summary of Cta-net: a Cnn-transformer Aggregation Network For Improving Multi-scale Feature Extraction, by Chunlei Meng et al.
CTA-Net: A CNN-Transformer Aggregation Network for Improving Multi-Scale Feature Extraction
by Chunlei Meng, Jiacheng Yang, Wei Lin, Bowen Liu, Hongda Zhang, chun ouyang, Zhongxue Gan
First submitted to arxiv on: 15 Oct 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 new paper proposes the CNN-Transformer Aggregation Network (CTA-Net) to efficiently combine convolutional neural networks (CNNs) and vision transformers (ViTs) for computer vision tasks. CTA-Net integrates long-range dependencies captured by transformers with localized features extracted by CNNs, allowing for effective processing of detailed local and broader contextual information. The paper also introduces two novel modules: the Light Weight Multi-Scale Feature Fusion Multi-Head Self-Attention (LMF-MHSA) module for multi-scale feature integration with reduced parameters, and the Reverse Reconstruction CNN-Variants (RRCV) module to enhance the embedding of CNNs within the transformer architecture. Experimental results on small-scale datasets show that CTA-Net achieves superior performance, fewer parameters, and greater efficiency compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new computer vision model called CTA-Net that combines two powerful techniques: convolutional neural networks (CNNs) and vision transformers (ViTs). This combination allows the model to learn from both local and global features. The authors also propose two new modules that help make their model more efficient. They tested their model on small datasets with great results, showing it can perform well while using fewer resources. |
Keywords
» Artificial intelligence » Cnn » Embedding » Self attention » Transformer
