Summary of Beyond Embeddings: the Promise Of Visual Table in Visual Reasoning, by Yiwu Zhong et al.
Beyond Embeddings: The Promise of Visual Table in Visual Reasoning
by Yiwu Zhong, Zi-Yuan Hu, Michael R. Lyu, Liwei Wang
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multimedia (cs.MM)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes Visual Table, a novel form of visual representation for visual reasoning. Unlike typical visual embeddings, Visual Tables are hierarchical descriptions of scenes featuring scene descriptions and object-centric descriptions covering categories, attributes, and knowledge. This format offers advantages such as interpretability and controllable editing. The generator is trained on a dataset with small-scale annotations to create Visual Tables. Results on 11 visual reasoning benchmarks show that Visual Tables significantly outperform previous representations, enhancing state-of-the-art multimodal large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way of understanding pictures called Visual Table. It helps computers understand what’s in the picture and why it matters. Unlike other ways of understanding pictures, this one includes words and descriptions that make sense to humans. This makes it better for tasks like image classification and visual question answering. |
Keywords
* Artificial intelligence * Image classification * Question answering
