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cloud.google.com·8h ago
AI Research
arXiv CS.AI·24d ago·5 min read
Cross-Modal Attention Acts as a Frequency Filter: Why Verbose Prompts Improve Robustness in Vision-Language Models
TL;DR
Cross-modal attention in vision-language models (VLMs) acts as a frequency filter, revealing that verbose prompts enhance robustness against image corruption. In contrast, complex queries can make models more fragile, leading to significant performance drops.
✦ Why It Matters
Use verbose prompts to improve the robustness of your vision-language models against image corruption.
Key Takeaways
- Use verbose prompts to enhance VLM robustness.
- Avoid complex queries that narrow attention focus.
- Implement padding in prompts for better accuracy.
- Test models like Qwen3-VL for improved performance.
How It Works
Cross-modal attention in VLMs functions as a frequency filter, where the wording of prompts influences the model's focus on visual information. Verbose prompts expand the model's attention across a wider range of visual frequencies, making it less sensitive to corruption.
In contrast, fine-grained questions concentrate attention on specific details, increasing vulnerability to errors when those details are corrupted.