Summary of Composing Open-domain Vision with Rag For Ocean Monitoring and Conservation, by Sepand Dyanatkar et al.
Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation
by Sepand Dyanatkar, Angran Li, Alexander Dungate
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed paper tackles the challenge of applying computer vision to ocean conservation by leveraging bottom-up, open-domain learning frameworks for image and video analysis. The authors aim to develop a scalable solution for species identification in marine applications, overcoming limitations posed by long-tailed distributions, generalization, and domain transfer. They introduce a preliminary demonstration using pretrained vision-language models (VLMs) combined with retrieval-augmented generation (RAG) as grounding. This approach enables emergent retrieval and prediction capabilities without domain-specific training or knowledge of the task itself. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to use computer vision in ocean conservation by learning from a wide range of images and videos, not just specific types of fish. This helps solve problems like identifying rare species and adapting models to new environments. The authors show that their approach works well for classifying fish in videos taken on fishing vessels. |
Keywords
» Artificial intelligence » Generalization » Grounding » Rag » Retrieval augmented generation
