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 segmentation, 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.

中文翻译：

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WaterNet：自适应匹配管道，用于分割外观不稳定的水

我们开发了一种新颖的网络来分割视频中具有明显外观差异的水。不同于使用预先训练的特征识别网络和几个先前的帧来指导分割的现有最新视频分割方法，我们通过考虑从当前帧中观察到的特征来适应对象的外观变化。当处理外观不均匀且动态变化的对象（例如水）时，我们的管道可以比现有算法产生更可靠，更准确的分割结果。