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October 9, 2025Open Access

Towards Omnimodal Expressions and Reasoning in Referring Audio-Visual Segmentation

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Proposed OmniAVS dataset enhances multimodal expressions and integrates reasoning in RAVS for improved performance.

Extensive experiments demonstrate that OISA outperforms existing methods in multimodal reasoning and segmentation.
The OmniAVS dataset includes 2,104 videos and 61,095 multimodal referring expressions for diverse content understanding.
New innovations in OmniAVS emphasize complex reasoning and world knowledge alongside standard audio-visual cues.
OISA leverages MLLM to facilitate improved comprehension and reasoning-based segmentation of audiovisual content.

Ying et al. (2025) studied this question.

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October 9, 2025Open Access

View Full Paper

Ask AI

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Authors

Proposed OmniAVS dataset enhances multimodal expressions and integrates reasoning in RAVS for improved performance.

Extensive experiments demonstrate that OISA outperforms existing methods in multimodal reasoning and segmentation.
The OmniAVS dataset includes 2,104 videos and 61,095 multimodal referring expressions for diverse content understanding.
New innovations in OmniAVS emphasize complex reasoning and world knowledge alongside standard audio-visual cues.
OISA leverages MLLM to facilitate improved comprehension and reasoning-based segmentation of audiovisual content.

Ying et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark