🚀 Introducing Robust-U1: Teaching MLLMs to Self-Recover Corrupted Visual Content
Multimodal Large Language Models (MLLMs) have achieved impressive visual understanding, yet they remain highly brittle under real-world corruptions—noise, blur, compression artifacts, adverse weather.
Standard MLLMs suffer dramatic performance drops, and existing robustness solutions come with fundamental limits: black‑box feature alignment lacks interpretability, while white‑box text reasoning cannot restore the lost pixel‑level visual details. This raises a crucial question:
🧐 Can MLLMs recover corrupted visual content by themselves?
If the answer is yes, we can move beyond merely “compensating” for corruption and instead build a more intrinsic, generalizable form of resilience. Robust-U1 is our answer to that question.
🛰️ Introducing Awesome-Remote-Sensing-Agents: The Largest Curated Collection of Intelligent Remote Sensing Agents
We are excited to share our new repository Awesome-Remote-Sensing-Agents – a comprehensive, community-driven collection of 100+ papers at the intersection of remote sensing and intelligent agents (LLMs, VLM, multi‑agent systems, etc.).
🤝 Join the Community! We warmly welcome contributions to keep this list up‑to‑date: 📝 Add missing papers via Pull Request 🏷️ Propose new or refined categories 🔗 Report broken links or outdated entries 💬 Discuss via GitHub Issues or contact the authors