Accurate segmentation in dynamic video environments is critical for applications like autonomous navigation and video analytics. This project introduced OSOSM, a one-shot learning approach for instance segmentation. The method enables robust segmentation by using a single labeled frame to adapt the model dynamically across sequences. The model further incorporates fast bilateral solver (FBS) for background alignment and a parallel contour detection branch for sharp object boundaries.