你能持续地听、定位和分割吗？面向音频视频分割的无示例持续学习基准
Can You Hear, Localize, and Segment Continually? An Exemplar-Free Continual Learning Benchmark for Audio-Visual Segmentation
AI总结：
本文提出无示例持续学习基准和ATLAS模型，通过低秩锚定缓解灾难性遗忘，验证了在动态环境下音频视频分割的持续学习能力。
摘要：
音频视频分割（AVS）旨在通过联合学习音频和视觉信号，生成视频中产生声音物体的像素级掩码。然而，现实环境本质上是动态的，导致音频和视觉分布随时间演变，这挑战了现有AVS系统所假设的静态训练设置。为解决这一差距，我们引入了首个无示例的持续学习基准，涵盖四个学习协议，覆盖单源和多源AVS数据集。我们进一步提出了一个强大的基线模型ATLAS，该模型使用音频引导的预融合条件化，通过投影的音频上下文在交叉模态注意力之前调节视觉特征通道。最后，我们通过引入低秩锚定（LRA）来缓解灾难性遗忘，该方法基于损失敏感性稳定适应的权重。广泛的实验表明，在多样化的持续学习场景中表现出竞争力，为终身音频视觉感知奠定了基础。代码可在${}^{*}$\footnote{论文正在审查} - \hyperlink{https://gitlab.com/viper-purdue/atlas}{https://gitlab.com/viper-purdue/atlas}。\n\n关键词：持续学习 \and 音频视频分割 \and 多模态学习
英文摘要：
Audio-Visual Segmentation (AVS) aims to produce pixel-level masks of sound producing objects in videos, by jointly learning from audio and visual signals. However, real-world environments are inherently dynamic, causing audio and visual distributions to evolve over time, which challenge existing AVS systems that assume static training settings. To address this gap, we introduce the first exemplar-free continual learning benchmark for Audio-Visual Segmentation, comprising four learning protocols across single-source and multi-source AVS datasets. We further propose a strong baseline, ATLAS, which uses audio-guided pre-fusion conditioning to modulate visual feature channels via projected audio context before cross-modal attention. Finally, we mitigate catastrophic forgetting by introducing Low-Rank Anchoring (LRA), which stabilizes adapted weights based on loss sensitivity. Extensive experiments demonstrate competitive performance across diverse continual scenarios, establishing a foundation for lifelong audio-visual perception. Code is available at${}^{*}$\footnote{Paper under review} - \hyperlink{https://gitlab.com/viper-purdue/atlas}{https://gitlab.com/viper-purdue/atlas} \keywords{Continual Learning \and Audio-Visual Segmentation \and Multi-Modal Learning}
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。
