Berlin Chen

Sparse Graph-Guided Mamba is proposed, a lightweight AVSE framework that integrates a sparse heterogeneous graph with a linear-complexity Mamba backbone that explicitly models modality-specific relations through content-adaptive attention and cross-frame audio-visual connections, while Mamba captures long-range temporal context.

Dramatically improve ASR for low-resource languages by cleverly leveraging readily available subtitles in higher-resource languages.