Unsupervised Video Object Segmentation
51 papers with code • 6 benchmarks • 8 datasets
The unsupervised scenario assumes that the user does not interact with the algorithm to obtain the segmentation masks. Methods should provide a set of object candidates with no overlapping pixels that span through the whole video sequence. This set of objects should contain at least the objects that capture human attention when watching the whole video sequence i.e objects that are more likely to be followed by human gaze.
Benchmarks
These leaderboards are used to track progress in Unsupervised Video Object Segmentation
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Datasets
DAVIS 2017
DAVIS 2016
FBMS
SegTrack-v2
Referring Expressions for DAVIS 2016 & 2017
MOSE
FBMS-59
BL30K
Most implemented papers
EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency
mfaisal59/EpONet
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WACV 2020
To handle the nonrigid background like a sea, we also propose a robust fusion mechanism between motion and appearance-based features.
Tukey-Inspired Video Object Segmentation
We investigate the problem of strictly unsupervised video object segmentation, i. e., the separation of a primary object from background in video without a user-provided object mask or any training on an annotated dataset.
Joint-task Self-supervised Learning for Temporal Correspondence
Our learning process integrates two highly related tasks: tracking large image regions \emph{and} establishing fine-grained pixel-level associations between consecutive video frames.
MAST: A Memory-Augmented Self-supervised Tracker
zlai0/MAST
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CVPR 2020
Recent interest in self-supervised dense tracking has yielded rapid progress, but performance still remains far from supervised methods.
D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos
schmiddo/d2conv3d
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WACV 2021
We further show that D2Conv3D out-performs trivial extensions of existing dilated and deformable convolutions to 3D.
Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
suhwan-cho/tmo
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Unsupervised video object segmentation (VOS) aims to detect the most salient object in a video sequence at the pixel level.
Online Unsupervised Video Object Segmentation via Contrastive Motion Clustering
Online unsupervised video object segmentation (UVOS) uses the previous frames as its input to automatically separate the primary object(s) from a streaming video without using any further manual annotation.
Video Object Segmentation using Supervoxel-Based Gerrymandering
Focusing on the problem of strictly unsupervised video object segmentation, we devise a method called supervoxel gerrymandering that links masks of foregroundness and backgroundness via local and non-local consensus measures.
SegFlow: Joint Learning for Video Object Segmentation and Optical Flow
This paper proposes an end-to-end trainable network, SegFlow, for simultaneously predicting pixel-wise object segmentation and optical flow in videos.
Unsupervised Video Object Segmentation for Deep Reinforcement Learning
vik-goel/MOREL
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NeurIPS 2018
The detection of moving objects is done in an unsupervised way by exploiting structure from motion.
