Summary of Progressive Multi-granular Alignments For Grounded Reasoning in Large Vision-language Models, by Quang-hung Le et al.
Progressive Multi-granular Alignments for Grounded Reasoning in Large Vision-Language Models
by Quang-Hung Le, Long Hoang Dang, Ngan Le, Truyen Tran, Thao Minh Le
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 proposes a novel framework called Progressive multi-granular Vision-Language alignments (PromViL) to enhance the performance of Large Vision-Language Models (LVLMs) in grounded compositional visual reasoning tasks. The approach constructs a hierarchical structure of multi-modal alignments, ranging from simple to complex concepts, and learns to leverage contextual information from lower levels to inform higher-level reasoning. The framework is evaluated on various visual grounding and compositional question answering tasks, outperforming baselines with significant improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand images better by creating a new way to match words and pictures. It’s like teaching a computer to read a picture storybook. The approach uses a special structure to connect simple ideas to more complex ones, and it works really well! By improving this ability, we can create smarter machines that can help us in many ways. |
Keywords
» Artificial intelligence » Grounding » Multi modal » Question answering
