Summary of Rethinking Decoders For Transformer-based Semantic Segmentation: a Compression Perspective, by Qishuai Wen and Chun-guang Li
Rethinking Decoders for Transformer-based Semantic Segmentation: A Compression Perspective
by Qishuai Wen, Chun-Guang Li
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 approach to Transformer-based semantic segmentation by exploring the connection between this task and compression techniques. Specifically, it derives a white-box, fully attentional DEcoder for PrIncipled semantiC segemenTation (DEPICT) that leverages Principal Component Analysis (PCA). The authors argue that the self-attention operator refines image embeddings to construct an ideal principal subspace, while cross-attention seeks to find a low-rank approximation. This results in compact representations for image embeddings as segmentation masks. Experiments on ADE20K dataset show that DEPICT outperforms its black-box counterpart, Segmenter, and is lightweight and robust. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using a special kind of artificial intelligence called Transformers to help computers understand what’s in pictures. Right now, the best way to do this uses something called attention, which helps the computer focus on important parts of the picture. But these methods don’t really explain why they work, so the authors want to change that. They think there might be a connection between understanding pictures and compressing data, like taking a big file and making it smaller. This idea leads them to create a new way of using attention called DEPICT. It works by refining what the computer sees in the picture, finding patterns in the data, and then turning that into a simple, easy-to-understand representation. When they test this method on pictures, it does better than other methods and is also more efficient. |
Keywords
» Artificial intelligence » Attention » Cross attention » Decoder » Pca » Principal component analysis » Self attention » Semantic segmentation » Transformer
