Google and UT Austin’s Game-Changing Approach Distills Vision-Language Models on Millions of Videos

In a new paper Distilling Vision-Language Models on Millions of Videos, a research team introduces a straightforward yet highly effective method to adapt image-based vision-language models to video. The approach involves generating high-quality pseudo-captions for millions of videos, outperforming state-of-the-art methods across various video-language benchmarks.

adapt ai art artificial intelligence austin benchmarks captions deep-neural-networks game google image language language models machine learning machine learning & data science ml model distillation paper quality research research team state team technology video videos vision vision-language models