Summary of Teochat: a Large Vision-language Assistant For Temporal Earth Observation Data, by Jeremy Andrew Irvin et al.
TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data
by Jeremy Andrew Irvin, Emily Ruoyu Liu, Joyce Chuyi Chen, Ines Dormoy, Jinyoung Kim, Samar Khanna, Zhuo Zheng, Stefano Ermon
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper introduces TEOChat, a novel vision and language assistant that can engage in conversations about temporal sequences of earth observation data. The authors adapt recent advances in natural image interpretation to handle sequential images, enabling tasks like building change and damage assessment, semantic change detection, and temporal scene classification. To train TEOChat, they curate an instruction-following dataset with single-image and temporal tasks. The model outperforms previous vision and language assistants on various spatial and temporal reasoning tasks, matching or surpassing specialist models trained for specific tasks. TEOChat also exhibits impressive zero-shot performance on change detection and question answering. Furthermore, it outperforms GPT-4o and Gemini 1.5 Pro on multiple temporal tasks and demonstrates stronger single-image capabilities than a comparable model on scene classification, visual question answering, and captioning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a super smart assistant that can talk about changes in the Earth’s surface over time. This paper creates such an assistant called TEOChat. It can look at multiple images taken at different times and understand what’s happening. For example, it can help detect when buildings are damaged or changed. The team trained TEOChat using lots of examples and showed that it’s really good at doing many tasks. In fact, it’s almost as good as experts who have spent a lot of time learning to do these tasks. They also released the data, model, and code so others can use it. |
Keywords
» Artificial intelligence » Classification » Gemini » Gpt » Question answering » Zero shot
