Summary of Prima: Multi-image Vision-language Models For Reasoning Segmentation, by Muntasir Wahed et al.
PRIMA: Multi-Image Vision-Language Models for Reasoning Segmentation
by Muntasir Wahed, Kiet A. Nguyen, Adheesh Sunil Juvekar, Xinzhuo Li, Xiaona Zhou, Vedant Shah, Tianjiao Yu, Pinar Yanardag, Ismini Lourentzou
First submitted to arxiv on: 19 Dec 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 addresses the gap between single-image pixel-grounding models and multi-image understanding models by introducing a novel Large Vision-Language Model (LVLM) called PRIMA. PRIMA integrates pixel-level grounding with robust multi-image reasoning capabilities to produce contextually rich, pixel-grounded explanations. The model includes an efficient vision module that queries fine-grained visual representations across multiple images, reducing the computational cost by 25.3%. To support training and evaluation, the authors curate a new benchmark dataset called M^4Seg, consisting of approximately 224K question-answer pairs that require fine-grained visual understanding across multiple images. Experimental results demonstrate PRIMA outperforms state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines understand pictures better by creating a new type of AI model called PRIMA. PRIMA can look at many pictures and tell us what’s going on in each one, which is important for things like searching for specific objects or scenes. The authors also created a special dataset to help train and test this new technology. They found that PRIMA is better than other similar models at doing this task. |
Keywords
» Artificial intelligence » Grounding » Language model
