Summary of Controllable Contextualized Image Captioning: Directing the Visual Narrative Through User-defined Highlights, by Shunqi Mao et al.
Controllable Contextualized Image Captioning: Directing the Visual Narrative through User-Defined Highlights
by Shunqi Mao, Chaoyi Zhang, Hang Su, Hwanjun Song, Igor Shalyminov, Weidong Cai
First submitted to arxiv on: 16 Jul 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 This paper presents Contextualized Image Captioning (CIC), a technique that generates image captions with specific contextual information. Building upon CIC, the authors introduce Controllable Contextualized Image Captioning (Ctrl-CIC), which emphasizes user-defined highlights to tailor captions. Two approaches are proposed: Prompting-based Controller (P-Ctrl) and Recalibration-based Controller (R-Ctrl). P-Ctrl prepends captions with highlight-driven prefixes, while R-Ctrl recalibrates encoder embeddings for highlighted tokens. An evaluator is designed using GPT-4V to assess caption quality alongside standard methods. Experimental results demonstrate the efficiency and effectiveness of Ctrl-CIC in achieving user-adaptive image captioning. Keywords: Contextualized Image Captioning, Controllable Contextualized Image Captioning, Prompting-based Controller, Recalibration-based Controller, GPT-4V, evaluator. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new way to write captions for images. Usually, image captioning just gives you a brief description of what’s in the picture. But this new method lets you specify what parts of the context are most important and asks the model to focus on those things when writing the caption. It uses two different approaches to do this: one adds special words at the beginning of the caption, and the other adjusts how it understands the text. They tested these methods and showed that they work well. This could be useful for people who want to make sure their image captions are accurate and focused on specific parts of the image. |
Keywords
» Artificial intelligence » Encoder » Gpt » Image captioning » Prompting
