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

Suyog Jain

Bo Xiong

Kristen Grauman


PAPER CODE TRAINING DATA CONTACT BIBTEX

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.

Overview


Additional Video Segmentation Results


Acknowledgement


This research is supported in part by ONR YIP N00014-12-1-0754.

The code and pre-trained models are freely available for research and academic purposes. However it's patent pending, so please contact us for any commercial use.