Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 5·5 min read
In-Context Collapse in Vision-Language Models and How to Mitigate it?
TL;DR
Vision-language models (VLMs) can experience an in-context collapse, leading to significant accuracy drops as more demonstrations are added. A proposed solution, CircA, mitigates this issue by enhancing integration at the vision-language interface.
✦ Why It Matters
Implement CircA to enhance the robustness of your vision-language models against in-context collapse.
Key Takeaways
- Monitor for in-context collapse in your VLMs as demonstrations increase.
- Use lightweight adapters to improve vision-language integration.
- Train CircA on synthetic tasks for better generalization.
- Differentiate between layers for in-context integration and weight-based learning.
How It Works
CircA is designed to enhance the integration between vision and language in models. By training on a synthetic task, it creates a robust framework that allows the model to better handle new, unseen tasks without suffering from in-context collapse.