Summary of Umie: Unified Multimodal Information Extraction with Instruction Tuning, by Lin Sun et al.
UMIE: Unified Multimodal Information Extraction with Instruction Tuning
by Lin Sun, Kai Zhang, Qingyuan Li, Renze Lou
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 proposed UMIE (Unified Multimodal Information Extractor) is a novel approach to multimodal information extraction that addresses the limitations of current methods by unifying three tasks as a generation problem using instruction tuning. This allows for effective extraction of both textual and visual mentions, outperforming state-of-the-art methods across six datasets on three tasks. The UMIE model demonstrates strong generalization in zero-shot settings, robustness to instruction variants, and interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary UMIE is a new way to extract information from pictures and text that combines three different tasks into one. This helps the model learn more about what’s important and can be applied to many different situations. It also does well even when it hasn’t seen something before, and can explain its answers in a way that makes sense. |
Keywords
» Artificial intelligence » Generalization » Instruction tuning » Zero shot
