Summary of Document Haystacks: Vision-language Reasoning Over Piles Of 1000+ Documents, by Jun Chen et al.
Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ Documents
by Jun Chen, Dannong Xu, Junjie Fei, Chun-Mei Feng, Mohamed Elhoseiny
First submitted to arxiv on: 23 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 abstract discusses large multimodal models (LMMs) that have made significant progress in vision-language understanding. However, these models face limitations when dealing with complex reasoning over a large number of images, which is crucial for real-world applications. To address this issue, the authors introduce two document haystack benchmarks, DocHaystack and InfoHaystack, designed to evaluate LMM performance on large-scale visual document retrieval and understanding. The authors also propose V-RAG, a novel framework that leverages multimodal vision encoders optimized for specific strengths and a question-document relevance module. V-RAG sets a new standard with significant improvements in Recall@1 on the challenging DocHaystack-1000 and InfoHaystack-1000 benchmarks. Integrating V-RAG with LMMs enables them to efficiently operate across thousands of images, yielding further improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large multimodal models (LMMs) are really smart computers that can understand pictures and words together. But they’re not very good at handling lots of pictures at once. To help LMMs get better, scientists created two new tests called DocHaystack and InfoHaystack. These tests show how well the models can find important information in a huge pile of images. The scientists also invented a new way to make LMMs work even better called V-RAG. This helps the models look through thousands of pictures quickly and accurately. |
Keywords
» Artificial intelligence » Language understanding » Rag » Recall
