Summary of Msdnet: Multi-scale Decoder For Few-shot Semantic Segmentation Via Transformer-guided Prototyping, by Amirreza Fateh et al.
MSDNet: Multi-Scale Decoder for Few-Shot Semantic Segmentation via Transformer-Guided Prototyping
by Amirreza Fateh, Mohammad Reza Mohammadi, Mohammad Reza Jahed Motlagh
First submitted to arxiv on: 17 Sep 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 This paper presents a novel approach to Few-shot Semantic Segmentation, which enables the segmentation of objects in query images with only a handful of annotated examples. The proposed framework builds upon the transformer architecture and introduces two key components: a spatial transformer decoder and a contextual mask generation module. These modules improve the relational understanding between support and query images, allowing for more accurate segmentation. Additionally, the framework incorporates features from different resolutions through a multi-scale decoder and integrates global features from intermediate encoder stages to enhance contextual understanding. The result is a lightweight model that achieves state-of-the-art results on benchmark datasets such as PASCAL-5^i and COCO-20^i in both 1-shot and 5-shot settings, with only 1.5 million parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand objects by teaching computers to quickly learn from a few examples of what an object looks like. The authors created a new way for computers to do this called Few-shot Semantic Segmentation. It’s like showing a child a few pictures and then asking them to identify the same object in other pictures. The authors used special computer tools, like transformers, to make it work. They also added some extra features to help the computer understand how objects relate to each other. This new way of doing things is much faster and uses less energy than previous methods. It’s even better than what other computers can do, which is really cool! |
Keywords
» Artificial intelligence » 1 shot » Decoder » Encoder » Few shot » Mask » Semantic segmentation » Transformer
