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arXiv 2409.00486cs.CVcs.LGcs.MMcs.SDeess.AS
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多尺度多实例视觉声源定位与分割

Multi-scale Multi-instance Visual Sound Localization and Segmentation

Carnegie Mellon University(卡内基梅隆大学);MBZUAI(穆罕默德·本·扎耶德人工智能大学);Xiaohongshu(小红书)

Shentong Mo, Haofan Wang

AI总结:

本文提出多尺度多实例视觉声源定位框架M2VSL,通过可学习多尺度视觉特征与多尺度多实例Transformer聚合跨模态表示,在多个基准上实现发声物体定位与分割的最先进性能。

摘要:

视觉声源定位是一个典型且具有挑战性的问题,其目标是预测视频中与声源对应的物体位置。以往方法主要利用全局音频与单一尺度视觉特征之间的音视频关联来定位每张图像中的发声物体。尽管这些方法取得了不错的性能,但它们忽略了对应图像的多尺度视觉特征,并且无法学习到与真值相比具有判别性的区域。为了解决这一问题,我们提出了一种新颖的多尺度多实例视觉声源定位框架,即M2VSL,该框架能够直接从输入图像中学习与声源相关的多尺度语义特征来定位发声物体。具体而言,我们的M2VSL利用可学习的多尺度视觉特征,在对应图像的多级位置上对齐音视频表示。我们还引入了一种新颖的多尺度多实例Transformer,用于动态聚合多尺度跨模态表示以实现视觉声源定位。我们在VGGSound-Instruments、VGG-Sound Sources和AVSBench基准上进行了大量实验。结果表明,所提出的M2VSL在发声物体定位和分割任务上能够取得最先进的性能。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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