Papers › Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation

Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation

4 Sep 2018ECCV 2018 9arXiv:1809.01125archive 2025-07-28

Yuan-Ting Hu, Jia-Bin Huang, Alexander G. Schwing

Unsupervised video segmentation plays an important role in a wide variety of applications from object identification to compression. However, to date, fast motion, motion blur and occlusions pose significant challenges. To address these challenges for unsupervised video segmentation, we develop a novel saliency estimation technique as well as a novel neighborhood graph, based on optical flow and edge cues. Our approach leads to significantly better initial foreground-background estimates and their robust as well as accurate diffusion across time. We evaluate our proposed algorithm on the challenging DAVIS, SegTrack v2 and FBMS-59 datasets. Despite the usage of only a standard edge detector trained on 200 images, our method achieves state-of-the-art results outperforming deep learning based methods in the unsupervised setting. We even demonstrate competitive results comparable to deep learning based methods in the semi-supervised setting on the DAVIS dataset.

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Tasks

Deep Learning Optical Flow Estimation Saliency Prediction Segmentation Semantic Segmentation Unsupervised Video Object Segmentation Video Object Segmentation Video Salient Object Detection Video Segmentation Video Semantic Segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Video Salient Object Detection	DAVSOD-Difficult20	MBNM	Average MAE	0.140	#4 of 8	Archive leaderboard	report
Video Salient Object Detection	DAVSOD-Difficult20	MBNM	S-Measure	0.561	#4 of 8	Archive leaderboard	report
Video Salient Object Detection	DAVSOD-Difficult20	MBNM	max E-measure	0.635	#4 of 8	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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