Summary of Raven: Multitask Retrieval Augmented Vision-language Learning, by Varun Nagaraj Rao et al.
RAVEN: Multitask Retrieval Augmented Vision-Language Learning
by Varun Nagaraj Rao, Siddharth Choudhary, Aditya Deshpande, Ravi Kumar Satzoda, Srikar Appalaraju
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 introduces RAVEN, a multitask retrieval-augmented vision-language model framework that enhances base models through efficient fine-tuning. The authors propose a novel approach to integrate retrieval-augmented samples without requiring additional parameters. This is achieved by fine-tuning the model on task-specific data, allowing it to acquire properties effective across multiple tasks. The results show significant performance improvements compared to non-retrieval baselines for image captioning and visual question answering tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to learn about anything in the world! Right now, our computers can’t understand everything because they need too much memory. One way to fix this is by using something called Retrieval-Augmented Generation (RAG). But we haven’t tried it with special computer vision models yet. Usually, these models are trained for one specific task, but that’s not very efficient. They also require a lot of extra processing power and new parameters. This paper shows how to make a better model by fine-tuning an existing computer vision model using RAG. The results show that this approach works really well for tasks like describing images and answering questions about what’s in those images. |
Keywords
» Artificial intelligence » Fine tuning » Image captioning » Language model » Question answering » Rag » Retrieval augmented generation
