Summary of Ctrlsynth: Controllable Image Text Synthesis For Data-efficient Multimodal Learning, by Qingqing Cao et al.
CtrlSynth: Controllable Image Text Synthesis for Data-Efficient Multimodal Learning
by Qingqing Cao, Mahyar Najibi, Sachin Mehta
First submitted to arxiv on: 15 Oct 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 The paper proposes a controllable image-text synthesis pipeline, CtrlSynth, to enhance multimodal learning in foundation models like CLIP. By decomposing visual semantics into basic elements, applying user-defined control policies, and recomposing them, CtrlSynth enables fine-grained manipulation of data synthesis for diverse applications. The framework leverages pretrained models like large language models or diffusion models to reason and recompose basic elements, producing natural and diverse synthetic samples. The authors demonstrate the effectiveness of CtrlSynth on 31 datasets across various vision and vision-language tasks, achieving improvements in zero-shot classification, image-text retrieval, and compositional reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a tool called CtrlSynth that helps make better pictures or words for machines to learn from. It breaks down images into simple parts and lets people control how these parts are mixed together to create new ones. This makes it easier to teach machines new things without needing lots of data. The authors tested their tool on many different types of pictures and words, and it worked well. |
Keywords
» Artificial intelligence » Classification » Semantics » Zero shot
