Training-Free Speech-Centric Omni Understanding with Frozen VLMs
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.…
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From the original article
1 min read at huggingface.co
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.
By Ankan Deria, Hanoona Rasheed, Xilin He · huggingface.co
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