Agreement-Based Audio-Visual Segmentation:Champion Report for the MeViS-Audio Track in the 8th LSVOS Challenge

• URL: http://arxiv.org/abs/2608.09475v1
• Date: Mon, 10 Aug 2026 11:43:31 GMT
• Title: Agreement-Based Audio-Visual Segmentation:Champion Report for the MeViS-Audio Track in the 8th LSVOS Challenge
• Abstract summary: Qwen3-ASR first converts speech into text.<n>We select the track that has the highest average mask agreement with the other candidates.<n>A video-level classifier combines visual, audio-visual, and within-video query scores to decide whether any target is present.
• Score: 46.49526425182438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The MeViS-Audio track asks a system to segment the objects described by a spoken motion expression throughout a video and to return empty masks when the described target is absent. We present a simple staged solution. Qwen3-ASR first converts speech into text. Several video mask tracks are then produced with complementary grounding and segmentation models. Instead of trusting a single prediction, we select the track that has the highest average mask agreement with the other candidates. A small set of explicit direction, count, and plural rules corrects queries that require more than ordinary single-object tracking. Finally, a video-level classifier combines visual, audio-visual, and within-video query scores to decide whether any target is present. The submitted system obtains 0.5952 J &F, 0.7931 no-target accuracy, 0.9205 target accuracy, and a final score of 0.769589. The challenge organizers notified our team that this result ranked first in the track.

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