Summary of Visually Descriptive Language Model For Vector Graphics Reasoning, by Zhenhailong Wang et al.
Visually Descriptive Language Model for Vector Graphics Reasoning
by Zhenhailong Wang, Joy Hsu, Xingyao Wang, Kuan-Hao Huang, Manling Li, Jiajun Wu, Heng Ji
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 addresses a long-standing challenge in large multimodal models (LMMs), which struggle to combine low-level visual perception with high-level language reasoning. Specifically, LMMs fail to precisely perceive geometric properties and solve visual reasoning problems, such as comparing shapes or solving puzzles. To study this limitation, the authors focus on vector graphics, a common format for web, design, and operating system applications. They propose the Visually Descriptive Language Model (VDLM), which introduces Primal Visual Description (PVD) as an intermediate textual representation to facilitate zero-shot generalization by foundation models like GPT-4o. PVD translates Scalable Vector Graphics (SVGs) into a structured text-based abstraction, enabling direct interpretation by LMMs. Experimental results show that VDLM significantly improves state-of-the-art LMMs on various multimodal perception and reasoning tasks without human-annotated data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can better understand pictures and words together. Right now, computers are good at recognizing objects or reading text, but they struggle to combine these skills. For example, if you ask a computer to compare two shapes, it might not be able to do it correctly. To fix this problem, the authors developed a new way for computers to understand vector graphics, which are used in many digital applications. They created a new model that can translate pictures into words, allowing computers to better understand and reason about visual information. |
Keywords
» Artificial intelligence » Generalization » Gpt » Language model » Zero shot
