Summary of Text-only Synthesis For Image Captioning, by Qing Zhou et al.
Text-only Synthesis for Image Captioning
by Qing Zhou, Junlin Huang, Qiang Li, Junyu Gao, Qi Wang
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 proposes Text-only Synthesis for Image Captioning (ToCa), an approach that relaxes the need for high-cost, large-scale annotation of good quality data. ToCa deconstructs caption text into structures and lexical words, which serve as inputs to a large language model to generate massive captions with various patterns. This method not only approaches but also surpasses the target domain, enhancing zero-shot generalization ability. The paper defines three synthesis scenarios: cross-domain, in-domain, and data-efficient, demonstrating the generalizability, transferability, and practicability of ToCa. Notably, ToCa achieves a nearly 5 CIDEr improvement for zero-shot cross-domain captioning and a maximum increase of over 20 CIDEr for data-efficient captioning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines create captions for images without needing lots of labeled training data. They created a new way to break down text into simple parts, like sentence structures and word meanings. These parts are used to train a language model to generate many captions with different patterns. This method works well even when the trained model hasn’t seen similar images before. The researchers tested their approach in different scenarios and showed that it can be useful for creating image captions. |
Keywords
» Artificial intelligence » Generalization » Image captioning » Language model » Large language model » Transferability » Zero shot
