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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Datasets
DAVIS 2017
DAVIS 2016
FBMS
SegTrack-v2
Referring Expressions for DAVIS 2016 & 2017
MOSE
FBMS-59
BL30K
Most implemented papers
Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection
shenjianbing/PDB-ConvLSTM
•
•
ECCV 2018
This paper proposes a fast video salient object detection model, based on a novel recurrent network architecture, named Pyramid Dilated Bidirectional ConvLSTM (PDB-ConvLSTM).
Extending Layered Models to 3D Motion
We consider the problem of inferring a layered representa-tion, its depth ordering and motion segmentation from a video in whichobjects may undergo 3D non-planar motion relative to the camera.
Unsupervised Online Video Object Segmentation with Motion Property Understanding
Moreover, our method achieves better performance than the best unsupervised offline algorithm on the DAVIS-2016 benchmark dataset.
Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting
A human teacher can show potential objects of interest to the robot, which is able to self adapt to the teaching signal without providing manual segmentation labels.
Unsupervised Moving Object Detection via Contextual Information Separation
antonilo/unsupervised_detection
•
•
CVPR 2019
We propose an adversarial contextual model for detecting moving objects in images.
RVOS: End-to-End Recurrent Network for Video Object Segmentation
Multiple object video object segmentation is a challenging task, specially for the zero-shot case, when no object mask is given at the initial frame and the model has to find the objects to be segmented along the sequence.
Learning Correspondence from the Cycle-Consistency of Time
xiaolonw/TimeCycle
•
•
CVPR 2019
We introduce a self-supervised method for learning visual correspondence from unlabeled video.
Self-supervised Learning for Video Correspondence Flow
zlai0/CorrFlow
•
•
Fourth, in order to shed light on the potential of self-supervised learning on the task of video correspondence flow, we probe the upper bound by training on additional data, \ie more diverse videos, further demonstrating significant improvements on video segmentation.
Learning Unsupervised Video Object Segmentation Through Visual Attention
This paper conducts a systematic study on the role of visual attention in Unsupervised Video Object Segmentation (UVOS) tasks.
A 3D Convolutional Approach to Spectral Object Segmentation in Space and Time
bit-ml/sfseg
•
•
Our method is based on the power iteration for finding the principal eigenvector of a matrix, which we prove is equivalent to performing a specific set of 3D convolutions in the space-time feature volume.
