VideoSAM: A Large Vision Foundation Model for High-Speed Video Segmentation
This addresses the problem of accurate segmentation in high-speed videos for scientific and industrial applications, but it is incremental as it adapts an existing foundation model.
The authors tackled high-speed video segmentation for analyzing dynamic physical processes like boiling heat transfer, and VideoSAM, a fine-tuned adaptation of the Segment Anything Model, significantly outperformed U-Net across four fluid environments.
High-speed video (HSV) segmentation is essential for analyzing dynamic physical processes in scientific and industrial applications, such as boiling heat transfer. Existing models like U-Net struggle with generalization and accurately segmenting complex bubble formations. We present VideoSAM, a specialized adaptation of the Segment Anything Model (SAM), fine-tuned on a diverse HSV dataset for phase detection. Through diverse experiments, VideoSAM demonstrates superior performance across four fluid environments -- Water, FC-72, Nitrogen, and Argon -- significantly outperforming U-Net in complex segmentation tasks. In addition to introducing VideoSAM, we contribute an open-source HSV segmentation dataset designed for phase detection, enabling future research in this domain. Our findings underscore VideoSAM's potential to set new standards in robust and accurate HSV segmentation. The code and dataset used in this study are available online at https://github.com/chikap421/videosam.