In this episode, we discuss Segment Anything with Multiple Modalities by Aoran Xiao, Weihao Xuan, Heli Qi, Yun Xing, Naoto Yokoya, Shijian Lu. The paper introduces MM-SAM, an extension of the Segment Anything Model (SAM) tailored for multi-modal data from various sensor suites, such as LiDAR plus RGB and thermal plus RGB. MM-SAM employs unsupervised cross-modal transfer and weakly-supervised multi-modal fusion to adapt efficiently to different sensor modalities. Extensive experiments validate that MM-SAM significantly outperforms the original SAM in robustness and segmentation accuracy across various sensors and modalities.
