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
Latest papers with no code
TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut
This method also achieves competitive results for unsupervised video object segmentation tasks with the DAVIS, SegTV2, and FBMS datasets.
Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier
We believe that we can select a better reference frame to achieve the better UVOS performance than using only the first frame or the entire video as a reference frame.
Learning To Segment Dominant Object Motion From Watching Videos
Existing deep learning based unsupervised video object segmentation methods still rely on ground-truth segmentation masks to train.
Video Salient Object Detection via Contrastive Features and Attention Modules
Video salient object detection aims to find the most visually distinctive objects in a video.
Deep Transport Network for Unsupervised Video Object Segmentation
The popular unsupervised video object segmentation methods fuse the RGB frame and optical flow via a two-stream network.
F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation
Specifically, our proposed network consists of three main parts: Siamese Encoder Module, Center Guiding Appearance Diffusion Module, and Dynamic Information Fusion Module.
DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping
Then, it uses the segments to learn object models that can be used for detection in a static image.
Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation
In this paper, we introduce a novel network, called discriminative feature network (DFNet), to address the unsupervised video object segmentation task.
Global Optimality Guarantees for Nonconvex Unsupervised Video Segmentation
In this paper, we consider the problem of unsupervised video object segmentation via background subtraction.
Key Instance Selection for Unsupervised Video Object Segmentation
After M-th frame, we select K IDs based on video saliency and frequency of appearance; then only these key IDs are tracked through the remaining frames.
