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arXiv · 2509.22740

Learning What To Hear: Boosting Sound-Source Association For Robust Audiovisual Instance Segmentation

Jinbae Seo·Hyeongjun Kwon·Kwonyoung Kim·Jiyoung Lee·Kwanghoon Sohn
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Abstract

Audiovisual instance segmentation (AVIS) requires accurately localizing and tracking sounding objects throughout video sequences. Existing methods suffer from visual bias stemming from two fundamental issues: uniform additive fusion prevents queries from specializing to different sound sources, while visual-only training objectives allow queries to converge to arbitrary salient objects. We propose Audio-Centric Query Generation using cross-attention, enabling each query to selectively attend to distinct sound sources and carry sound-specific priors into visual decoding. Additionally, we introduce Sound-Aware Ordinal Counting (SAOC) loss that explicitly supervises sounding object numbers through ordinal regression with monotonic consistency constraints, preventing visual-only convergence during training. Experiments on AVISeg benchmark demonstrate consistent improvements: +1.64 mAP, +0.6 HOTA, and +2.06 FSLA, validating that query specialization and explicit counting supervision are crucial for accurate audiovisual instance segmentation.

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Jinbae Seo, Hyeongjun Kwon, Kwonyoung Kim, Jiyoung Lee, Kwanghoon Sohn. 2026-01-29. Learning What To Hear: Boosting Sound-Source Association For Robust Audiovisual Instance Segmentation. https://arxiv.org/abs/2509.22740

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