# Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation Source: [Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:21:52.901Z Authors: Ye Wang, Jongmoo Choi, Yueru Chen, Qin Huang, Siyang Li, Ming-Sui Lee, C.-C. Jay Kuo Published: 2018-12-13T00:13:29.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()