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We propose an end-to-end learning framework for segmenting generic objects invideos. Our method learns to combine appearance and motion information toproduce pixel level segmentation masks for all prominent objects in videos. Weformulate this task as a structured prediction problem and design a two-streamfully convolutional neural network which fuses together motion and appearancein a unified framework. Since large-scale video datasets with pixel levelsegmentations are problematic, we show how to bootstrap weakly annotated videostogether with existing image recognition datasets for training. Throughexperiments on three challenging video segmentation benchmarks, our methodsubstantially improves the state-of-the-art for segmenting generic (unseen)objects. Code and pre-trained models are available on the project website.

FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos