Learning Video Object Segmentation with Visual Memory

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This paper addresses the task of segmenting moving objects in unconstrained videos.

• We introduce a novel two-stream neural network with an explicit memory module to achieve this.
• The two streams of the network encode spatial and temporal features in a video sequence respectively, while the memory module captures the evolution of objects over time.
• The module to build a "visual memory" in video, i.e., a joint representation of all the video frames, is realized with a convolutional recurrent unit learned from a small number of training video sequences.