Multi-scale Multi-instance Visual Sound Localization and Segmentation

• URL: http://arxiv.org/abs/2409.00486v1
• Date: Sat, 31 Aug 2024 15:43:22 GMT
• Title: Multi-scale Multi-instance Visual Sound Localization and Segmentation
• Authors: Shentong Mo, Haofan Wang,
• Abstract summary: We propose a novel multi-scale visual sound localization framework, namely M2VSL. M2VSL learns multi-scale semantic features associated with sound sources from the input image to localize sounding objects. We conduct extensive experiments on VGGSound-Instruments, VGG-Sound Sources, and AVSBench benchmarks.
• Score: 15.624453757710802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual sound localization is a typical and challenging problem that predicts the location of objects corresponding to the sound source in a video. Previous methods mainly used the audio-visual association between global audio and one-scale visual features to localize sounding objects in each image. Despite their promising performance, they omitted multi-scale visual features of the corresponding image, and they cannot learn discriminative regions compared to ground truths. To address this issue, we propose a novel multi-scale multi-instance visual sound localization framework, namely M2VSL, that can directly learn multi-scale semantic features associated with sound sources from the input image to localize sounding objects. Specifically, our M2VSL leverages learnable multi-scale visual features to align audio-visual representations at multi-level locations of the corresponding image. We also introduce a novel multi-scale multi-instance transformer to dynamically aggregate multi-scale cross-modal representations for visual sound localization. We conduct extensive experiments on VGGSound-Instruments, VGG-Sound Sources, and AVSBench benchmarks. The results demonstrate that the proposed M2VSL can achieve state-of-the-art performance on sounding object localization and segmentation.

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