Summary of Can Visual Language Models Resolve Textual Ambiguity with Visual Cues? Let Visual Puns Tell You!, by Jiwan Chung et al.
Can visual language models resolve textual ambiguity with visual cues? Let visual puns tell you!
by Jiwan Chung, Seungwon Lim, Jaehyun Jeon, Seungbeen Lee, Youngjae Yu
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents Understanding Pun with Image Explanations (UNPIE), a novel benchmark designed to assess the impact of multimodal inputs in resolving lexical ambiguities. The authors use puns as the ideal subject for evaluation due to their intrinsic ambiguity, and create a dataset of 1,000 puns each accompanied by an image that explains both meanings. The results show that various Socratic Models and Visual-Language Models improve over text-only models when given visual context, particularly as task complexity increases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores whether machines can achieve multimodal understanding like humans do. It creates a special test to see how well different computer programs do when shown pictures along with words. The test is called UNPIE and uses puns (which are tricky to understand) to see if the programs can figure out what they mean. The results show that some types of computer models do better when they have both text and images to work with. |
