Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation
arXiv:1812.05206
Abstract
One major technique debt in video object segmentation is to label the object masks for training instances. As a result, we propose to prepare inexpensive, yet high quality pseudo ground truth corrected with motion cue for video object segmentation training. Our method conducts semantic segmentation using instance segmentation networks and, then, selects the segmented object of interest as the pseudo ground truth based on the motion information. Afterwards, the pseudo ground truth is exploited to finetune the pretrained objectness network to facilitate object segmentation in the remaining frames of the video. We show that the pseudo ground truth could effectively improve the segmentation performance. This straightforward unsupervised video object segmentation method is more efficient than existing methods. Experimental results on DAVIS and FBMS show that the proposed method outperforms state-of-the-art unsupervised segmentation methods on various benchmark datasets. And the category-agnostic pseudo ground truth has great potential to extend to multiple arbitrary object tracking.
16 pages, 7 figures, 6 tables, conference
References in corpus (10)
- TensorFlow: A system for large-scale machine learning
- Fully Convolutional Networks for Semantic Segmentation
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
- Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
- Simultaneous Detection and Segmentation
- FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos
- Video Object Segmentation with Re-identification
- Learning Video Object Segmentation with Visual Memory
- Lucid Data Dreaming for Video Object Segmentation
- Semantic Segmentation with Reverse Attention