Summary of Rs-moe: a Vision-language Model with Mixture Of Experts For Remote Sensing Image Captioning and Visual Question Answering, by Hui Lin et al.
RS-MoE: A Vision-Language Model with Mixture of Experts for Remote Sensing Image Captioning and Visual Question Answering
by Hui Lin, Danfeng Hong, Shuhang Ge, Chuyao Luo, Kai Jiang, Hao Jin, Congcong Wen
First submitted to arxiv on: 3 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 RS-MoE, a novel Mixture of Expert based VLM specifically designed for remote sensing image captioning. The model incorporates an Instruction Router and multiple lightweight Large Language Models as expert models, which generate specific prompts tailored to each LLM to focus on distinct aspects of the task. This design enhances specificity, accuracy, and scalability. The authors present a two-stage training strategy to prevent performance degradation due to sparsity and fine-tune their model on the RSICap dataset. Experimental results demonstrate state-of-the-art performance in generating precise captions, with the RS-MoE-1B variant achieving comparable performance to 13B VLMs. The model also shows promising generalization capabilities on the Remote Sensing Visual Question Answering task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to make computers better at understanding and describing pictures taken from space. Right now, computers struggle to write good descriptions of these pictures. Some smart people have been working on fixing this problem by using special computer models called VLMs (Very Large Models). These models are very good at writing descriptions of text, but they don’t work as well for pictures. The authors of this paper came up with a new idea to make these models better at describing pictures taken from space. They created a model called RS-MoE that uses multiple smaller models working together to come up with the best description. This helps the computer focus on different parts of the picture and write a more accurate description. The authors tested their model and it worked really well! It even did as well as much bigger and more powerful models. |
Keywords
» Artificial intelligence » Generalization » Image captioning » Question answering
