Never having seen an object and heard its sound simultaneously, can the model still accurately localize its visual position from the input audio? In this work, we concentrate on the Audio-Visual Localization and Segmentation tasks but under the demanding zero-shot and few-shot scenarios. To achieve this goal, different from existing approaches that mostly employ the encoder-fusion-decoder paradigm to decode localization information from the fused audio-visual feature, we introduce the encoder-prompt-decoder paradigm, aiming to better fit the data scarcity and varying data distribution dilemmas with the help of abundant knowledge from pre-trained models. Specifically, we first propose to construct Semantic-aware Audio Prompt (SAP) to help the visual foundation model focus on sounding objects, meanwhile, the semantic gap between the visual and audio modalities is also encouraged to shrink. Then, we develop a Correlation Adapter (ColA) to keep minimal training efforts as well as maintain adequate knowledge of the visual foundation model. By equipping with these means, extensive experiments demonstrate that this new paradigm outperforms other fusion-based methods in both the unseen class and cross-dataset settings. We hope that our work can further promote the generalization study of Audio-Visual Localization and Segmentation in practical application scenarios.

翻译：从未同时目睹物体并听到其声音，模型能否仍能根据输入音频准确定位其视觉位置？本文聚焦于视听定位与分割任务，但在极具挑战性的零样本和小样本场景下展开研究。为实现该目标，不同于现有方法主要采用编码器-融合-解码器范式从融合视听特征中解码定位信息，我们引入编码器-提示-解码器范式，旨在借助预训练模型丰富的知识，更好地应对数据稀缺和分布变化等问题。具体而言，我们首先提出构建语义感知音频提示（SAP），帮助视觉基础模型聚焦于发声物体，同时鼓励缩小视觉与音频模态间的语义差距。随后，我们开发了相关性适配器（ColA），在保持最小训练开销的同时，充分保留视觉基础模型的知识。通过配备这些手段，大量实验表明，这种新范式在未见类别和跨数据集设置下均优于其他基于融合的方法。我们期望本研究能进一步推动视听定位与分割在实际应用场景中的泛化研究。