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- W4295276072 abstract "We propose an end-to-end learning framework for segmenting generic objects in videos. Our method learns to combine appearance and motion information to produce pixel level segmentation masks for all prominent objects in videos. We formulate this task as a structured prediction problem and design a two-stream fully convolutional neural network which fuses together motion and appearance in a unified framework. Since large-scale video datasets with pixel level segmentations are problematic, we show how to bootstrap weakly annotated videos together with existing image recognition datasets for training. Through experiments on three challenging video segmentation benchmarks, our method substantially improves the state-of-the-art for segmenting generic (unseen) objects. Code and pre-trained models are available on the project website." @default.
- W4295276072 created "2022-09-12" @default.
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- W4295276072 date "2017-01-19" @default.
- W4295276072 modified "2023-09-25" @default.
- W4295276072 title "FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos" @default.
- W4295276072 doi "https://doi.org/10.48550/arxiv.1701.05384" @default.
- W4295276072 hasPublicationYear "2017" @default.
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