Robust-U1: Can MLLMs Self-Recover Corrupted Visual Content for Robust Understanding?

About this episode

This episode discusses the Robust-U1 framework that enables Multimodal Large Language Models to self-recover corrupted visual content for improved understanding.

🤗 Upvotes: 71 | cs.CV, cs.AI, cs.CL Authors: Jiaqi Tang, Jianmin Chen, Youyang Zhai, Wei Wei, Runtao Liu, Mengjie Zhao, Xiangyu Wu, Qingfa Xiao, Qifeng Chen Title: Robust-U1: Can MLLMs Self-Recover Corrupted Visual Content for Robust Understanding? Arxiv: http://arxiv.org/abs/2606.08063v1 Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in visual understanding, yet their performance degrades significantly under real-world visual corruptions. While existing robustness enhancement approaches exist, they are limited: black-box feature alignment lacks interpretability, and white-box text-based reasoning cannot restore lost pixel-level details. This work investigates a fundamental research question: Can MLLMs recover corrupted visual content by themselves? To address this, we propose Robust-U1, a novel framework that equips MLLMs with explicit visual self-recovery capability for robust understanding. The approach comprises three core stages: supervised fine-tuning for initial reconstruction, reinforcement learning with dual rewards (pixel-level SSIM and semantic-level CLIP similarity) for aligning high visual quality, and multimodal reasoning…

People in this episode

Hosts: Jingwen Liang, Gengyu Wang

Topics covered

• Multimodal Large Language Models
• Visual Understanding
• Robustness Enhancement
• Self-Recovery
• Reinforcement Learning
• Visual Corruption

Keywords

• MLLMs
• visual content recovery
• robust understanding
• reinforcement learning
• semantic similarity
• pixel-level SSIM
• adversarial corruptions
• VQA benchmarks

Mentioned in this episode

Organizations: Arxiv

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