The primary aim of Audio-Visual Segmentation (AVS) is to precisely identify and locate auditory elements within visual scenes by accurately predicting segmentation masks at the pixel level. Achieving this involves comprehensively considering data and model aspects to address this task effectively. This study presents a lightweight approach, SAVE, which efficiently adapts the pre-trained segment anything model (SAM) to the AVS task. By incorporating an image encoder adapter into the transformer blocks to better capture the distinct dataset information and proposing a residual audio encoder adapter to encode the audio features as a sparse prompt, our proposed model achieves effective audio-visual fusion and interaction during the encoding stage. Our proposed method accelerates the training and inference speed by reducing the input resolution from 1024 to 256 pixels while achieving higher performance compared with the previous SOTA. Extensive experimentation validates our approach, demonstrating that our proposed model outperforms other SOTA methods significantly. Moreover, leveraging the pre-trained model on synthetic data enhances performance on real AVSBench data, achieving 84.59 mIoU on the S4 (V1S) subset and 70.28 mIoU on the MS3 (V1M) set with only 256 pixels for input images. This increases up to 86.16 mIoU on the S4 (V1S) and 70.83 mIoU on the MS3 (V1M) with inputs of 1024 pixels.
Social Networks and Human-Centered Computing Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica（中央研究院资讯科学研究所台湾国际研究生计划社交网络与人本计算项目）；Department of Computer Science, National Chengchi University（国立政治大学资讯科学系）；Institute of Information Systems and Applications, National Tsing Hua University（国立清华大学资讯系统与应用研究所）；National Institute of Informatics（国立情报学研究所）；Department of Electrical Engineering and the Institute of Communications Engineering, National Tsing Hua University（国立清华大学电机工程学系与通讯工程研究所）；Research Center for Information Technology Innovation, Academia Sinica（中央研究院资讯科技创新研究中心）；Institute of Information Science, Academia Sinica（中央研究院资讯科学研究所）
AuralSAM2: Enabling SAM2 Hear Through Pyramid Audio-Visual Feature Prompting
Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Australian Institute for Machine Learning, Adelaide University（阿德莱德大学人工智能研究所）；Stanford University（斯坦福大学）；University of Central Florida（佛罗里达中央大学）；University of Surrey（萨里大学）
ASI-Seg: Audio-Driven Surgical Instrument Segmentation with Surgeon Intention Understanding
Centre for Artificial Intelligence and Robotics (CAIR), Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；Chinese University of Hong Kong（香港中文大学）
