Mosaic learns sparse image-text latents for VLMs
Researchers introduced MOSAIC, a new method to understand vision-language models (VLMs). It creates a shared sparse latent space between images and text concepts. This enables localized influence attribution across the model's training data without degrading performance. MOSAIC was tested on Qwen and Gemma for tasks like captioning, X-ray understanding, and visual QA. The method shows how VLM…
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Key points
- MOSAIC learns sparse shared latent space between images and text
- Localized influence attribution without degrading model performance
- Improves localization accuracy over existing methods
Read the original at arXiv cs.AI · by Laura Yao, Shixing Yu, Jie Peng, Promit Ghosal, Kyra Gan, Tianlong Chen primary sourceOpen source ↗
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