Ming Li

Visual textualization and image-native modeling outperform traditional text-only methods in predicting item difficulty, revealing the critical role of visual evidence in assessment design.

Achieving over 82% output correctness, this new benchmark and model redefine the standards for audio-visual target speaker extraction by effectively integrating visual cues.

LLMs can achieve state-of-the-art audio-visual speech recognition by sparsely aligning modalities and refining with visual unit guidance, substantially boosting robustness in noisy environments.