Summary of Is Your Text-to-image Model Robust to Caption Noise?, by Weichen Yu et al.
Is Your Text-to-Image Model Robust to Caption Noise?
by Weichen Yu, Ziyan Yang, Shanchuan Lin, Qi Zhao, Jianyi Wang, Liangke Gui, Matt Fredrikson, Lu Jiang
First submitted to arxiv on: 27 Dec 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 study investigates the impact of Vision Language Models’ (VLMs) caption hallucinations on text-to-image generation performance. Researchers develop a comprehensive dataset of VLM-generated captions and analyze how caption quality influences model outputs during fine-tuning. Findings reveal that VLM confidence scores are reliable indicators for detecting noise-related patterns, and even subtle variations in caption fidelity have significant effects on learned representations’ quality. The study emphasizes the profound impact of caption quality on model performance and proposes a new approach leveraging VLM confidence score to mitigate caption noise, enhancing T2I models’ robustness against hallucination. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how language models generate captions that don’t match what’s actually in an image. It finds that this “hallucination” affects the quality of the generated images and makes them less accurate. The researchers also discover that the model’s confidence scores can help detect when this is happening. They suggest a new way to fix this problem by using these confidence scores, making language models better at generating accurate images. |
Keywords
» Artificial intelligence » Fine tuning » Hallucination » Image generation
