Summary of Groundhog: Grounding Large Language Models to Holistic Segmentation, by Yichi Zhang et al.
GROUNDHOG: Grounding Large Language Models to Holistic Segmentation
by Yichi Zhang, Ziqiao Ma, Xiaofeng Gao, Suhaila Shakiah, Qiaozi Gao, Joyce Chai
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 GROUNDHOG, a multimodal large language model that learns to ground language in pixel-level representations. Unlike existing models, GROUNDHOG incorporates masked feature extraction and converts features into visual entity tokens for improved fine-grained visual understanding and diagnosis. The authors develop M3G2, a new dataset with rich annotations, and train GROUNDHOG on this dataset. The results show that GROUNDHOG outperforms other models on various language grounding tasks without requiring task-specific fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching computers to understand the relationship between words and images. Most computer models are good at understanding what words mean, but struggle when it comes to connecting those words to specific objects or locations in an image. The authors have developed a new kind of model called GROUNDHOG that can do this better than other models. They’ve also created a special dataset with lots of examples and labels to help train the model. This means that computers will be able to understand images more accurately and make better decisions based on what they see. |
Keywords
» Artificial intelligence » Feature extraction » Fine tuning » Grounding » Large language model
