Summary of World to Code: Multi-modal Data Generation Via Self-instructed Compositional Captioning and Filtering, by Jiacong Wang et al.
World to Code: Multi-modal Data Generation via Self-Instructed Compositional Captioning and Filtering
by Jiacong Wang, Bohong Wu, Haiyong Jiang, Xun Zhou, Xin Xiao, Haoyuan Guo, Jun Xiao
First submitted to arxiv on: 30 Sep 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 A novel approach to generating high-quality synthetic data for Vision-Language Models (VLMs) is proposed in this paper, which leverages the VLM itself to extract cross-modal information via different prompts and filter the generated outputs again via a consistency filtering strategy. The resulting pipeline, called World to Code (W2C), organizes the final generation output into a Python code format. Experiments demonstrate the high quality of W2C by improving various existing visual question answering and visual grounding benchmarks across different VLMs. Furthermore, the paper highlights the new code parsing ability of VLMs, which presents better cross-modal equivalence than the commonly used detail caption ability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research focuses on creating a better way to generate data for Vision-Language Models (VLMs). Right now, people use a mix of computer programs and humans to make this data. But this can be expensive and not always accurate. The researchers created a new process called World to Code (W2C) that uses the VLM itself to get information from different types of prompts and then filters the results for consistency. This makes better data that can improve how well VLMs do certain tasks. |
Keywords
» Artificial intelligence » Grounding » Parsing » Question answering » Synthetic data
