Training-Free Speech-Centric Omni Understanding with Frozen VLMs
Audio-visual understanding remains challenging because models must jointly interpret spoken content, visual events, and their temporal relationships. Existing omni models typically introduce dedicated audio encoders and rely on expensive audio-video-text training, tightly coupling omni capability to specific VLM backbones and potentially weakening their existing visual and reasoning abilities. This raises three questions: whether native omni training is necessary for every new VLM, whether speech-centric omni capability can be added while preserving the original backbone, and where richer acoustic representations remain essential. We introduce Training-Free Omni (TFO), a plug-and-play fra...
| Authors | Ankan Deria, Hanoona Rasheed, Xilin He et al. |
|---|---|
| Primary category | eess.AS |
| Published | 7 August 2026 |
| Last updated at source | 7 August 2026 |
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