Audiovisual segmentation (AVS) is a challenging task that aims to segment visual objects in videos according to their associated acoustic cues. With multiple sound sources and background disturbances involved, establishing robust correspondences between audio and visual contents poses unique challenges due to (1) complex entanglement across sound sources and (2) frequent changes in the occurrence of distinct sound events. Assuming sound events occur independently, the multi-source semantic space can be represented as the Cartesian product of single-source sub-spaces. We are motivated to decompose the multi-source audio semantics into single-source semantics for more effective interactions with visual content. We propose a semantic decomposition method based on product quantization, where the multi-source semantics can be decomposed and represented by several disentangled and noise-suppressed single-source semantics. Furthermore, we introduce a global-to-local quantization mechanism, which distills knowledge from stable global (clip-level) features into local (frame-level) ones, to handle frequent changes in audio semantics. Extensive experiments demonstrate that our semantically decomposed audio representation significantly improves AVS performance, e.g., +21.2% mIoU on the challenging AVS-Semantic benchmark with ResNet50 backbone. https://github.com/lxa9867/QSD.

翻译：视听分割（AVS）是一项具有挑战性的任务，旨在根据视频中与视觉对象相关的声学线索对其进行分割。由于涉及多个声源和背景干扰，建立音频与视觉内容之间的鲁棒对应关系面临独特挑战，原因包括：（1）声源间的复杂纠缠，以及（2）不同声音事件出现频率的频繁变化。假设声音事件独立发生，多源语义空间可表示为单源子空间的笛卡尔积。我们受到启发，将多源音频语义分解为单源语义，以实现与视觉内容的更有效交互。我们提出一种基于乘积量化的语义分解方法，该方法可将多源语义分解并表示为若干解耦且噪声抑制的单源语义。此外，我们引入全局到局部量化机制，将稳定全局（片段级）特征的知识蒸馏至局部（帧级）特征，以处理音频语义的频繁变化。大量实验表明，我们的语义分解音频表示显著提升了AVS性能，例如在采用ResNet50骨干网络的挑战性AVS-Semantic基准上，mIoU提升了21.2%。https://github.com/lxa9867/QSD。