Summary of Exploring Multi-grained Concept Annotations For Multimodal Large Language Models, by Xiao Xu et al.
Exploring Multi-Grained Concept Annotations for Multimodal Large Language Models
by Xiao Xu, Tianhao Niu, Yuxi Xie, Libo Qin, Wanxiang Che, Min-Yen Kan
First submitted to arxiv on: 8 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 A multimodal large language model (MLLM) excels in vision-language tasks by pre-training on coarse-grained concept annotations. Researchers hypothesize that integrating fine-grained concept annotations will further improve performance, as both data granularities complement each other in terms of breadth and depth in concept representation. The study introduces a new dataset featuring multimodal multi-grained concept annotations (MMGiC) for MLLMs, exploring the impact of different data recipes on multimodal comprehension and generation. Analyses reveal that multi-grained concept annotations integrate and complement each other, under a structured template and general MLLM framework. The research demonstrates the potential of MMGiC to help MLLMs better locate and learn concepts, aligning vision and language at multiple granularities. Validation is shown through fair comparison and effective collaboration between MMGiC and image-caption data on 12 multimodal comprehension and generation benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a new way to train machines that can understand images and text. They think this will help the machines get better at understanding concepts by giving them different levels of information about what’s in an image. The researchers made a special dataset with this information, which they call MMGiC (multimodal multi-grained concept annotations). They tested how well this worked and found that it helped the machines do better on tasks like recognizing objects in images or generating captions for those same images. |
Keywords
» Artificial intelligence » Large language model
