video segmentation, water segmentation, appearance adaptation
We develop a novel network to segment water with significant appearance variation in videos. Unlike existing state-of-the-art video segmentation approaches that use a pre-trained feature recognition network and several previous frames to guide seg-mentation, we accommodate the object’s appearance variation by considering features observed from the current frame. When dealing with segmentation of objects such as water, whose appearance is non-uniform and changing dynamically, our pipeline can produce more reliable and accurate segmentation results than existing algorithms.
Tsinghua University Press
Yongqing Liang, Navid Jafari, Xing Luo et al. WaterNet: An adaptive matching pipeline for segmenting water with volatile appearance. Computational Visual Media 2020, 6(1): 65-78.