Google and UT Austin’s Game-Changing Approach Distills Vision-Language Models on Millions of Videos | Synced
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...
Source: Synced | AI Technology & Industry Review
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.