Summary of Revisiting the Integration Of Convolution and Attention For Vision Backbone, by Lei Zhu et al.
Revisiting the Integration of Convolution and Attention for Vision Backbone
by Lei Zhu, Xinjiang Wang, Wayne Zhang, Rynson W. H. Lau
First submitted to arxiv on: 21 Nov 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 proposes a novel integration scheme for building vision backbones, combining convolutional (Conv) and multi-head self-attentive (MHS) mechanisms. While some works apply these methods simultaneously at the finest pixel granularity, this approach is inefficient and limits scalability with increasing input resolutions. The authors introduce a new method, called GLMix, which uses Convs and MHSs in parallel at different granularity levels. Specifically, they use Convs for local features on a fine-grained regular grid and MHSs for global features on a coarse-grained set of semantic slots. A soft clustering and dispatching module bridges the two representations, enabling local-global fusion. The authors demonstrate the effectiveness of GLMix through extensive experiments on various vision tasks, achieving state-of-the-art performance while being more efficient than recent backbones. The paper also visualizes meaningful semantic grouping effects using IN1k classification supervision, potentially inspiring new weakly-supervised semantic segmentation approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a problem in computer vision where it’s hard to make models bigger without making them slow. They propose a new way of combining two existing techniques: convolutional and attention-based methods. Instead of using both at the same time, they use them separately for different parts of an image. One part is looked at in detail with convolutions, while another part is seen as a whole with attention. This makes it possible to make bigger models without slowing them down. The authors test their method on many vision tasks and show that it performs well compared to other state-of-the-art methods. |
Keywords
» Artificial intelligence » Attention » Classification » Clustering » Semantic segmentation » Supervised
