Summary of Generative Model-based Fusion For Improved Few-shot Semantic Segmentation Of Infrared Images, by Junno Yun et al.
Generative Model-Based Fusion for Improved Few-Shot Semantic Segmentation of Infrared Images
by Junno Yun, Mehmet Akçakaya
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 tackles the challenge of few-shot segmentation (FSS) in infrared (IR) imaging, a crucial task for autonomous driving, fire safety, and defense applications. Despite its importance, existing FSS models require paired visible RGB images, which is impractical or impossible in many scenarios. To overcome this limitation, the authors propose novel strategies that utilize generative modeling and fusion techniques to synthesize auxiliary data and augment IR images. The proposed methods aim to capture relationships between support and query sets by synthesizing channel information and addressing data scarcity through IR data synthesis. A novel fusion ensemble module is also introduced to integrate different modalities. The authors evaluate their approach on various IR datasets, achieving state-of-the-art (SOTA) performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in computer vision: how to quickly teach a machine to recognize objects in infrared images. Infrared cameras are used in self-driving cars, fire detection, and military applications. But there’s a catch – the images are very different from regular pictures, which makes it hard for machines to learn. The authors come up with new ideas to help machines learn faster using less data. They suggest creating fake data to help the machine understand what objects look like in infrared images and also use that data to make the training process more robust. The new approach is tested on different types of infrared images and performs better than previous methods. |
Keywords
» Artificial intelligence » Few shot
