Browse SoTA > Computer Vision > Video Object Segmentation > Unsupervised Video Object Segmentation

Unsupervised Video Object Segmentation

21 papers with code · Computer Vision

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

Greatest papers with code

See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks

CVPR 2019 carrierlxk/COSNet

We introduce a novel network, called CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view.

SEMANTIC SEGMENTATION UNSUPERVISED VIDEO OBJECT SEGMENTATION VIDEO SEMANTIC SEGMENTATION

RVOS: End-to-End Recurrent Network for Video Object Segmentation

CVPR 2019 imatge-upc/rvos

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.

UNSUPERVISED VIDEO OBJECT SEGMENTATION YOUTUBE-VOS

Anchor Diffusion for Unsupervised Video Object Segmentation

ICCV 2019 yz93/anchor-diff-VOS

Unsupervised video object segmentation has often been tackled by methods based on recurrent neural networks and optical flow.

Ranked #3 on Unsupervised Video Object Segmentation on DAVIS 2016 (using extra training data)

OPTICAL FLOW ESTIMATION SEMANTIC SEGMENTATION UNSUPERVISED VIDEO OBJECT SEGMENTATION VIDEO SEMANTIC SEGMENTATION

Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection

ECCV 2018 shenjianbing/PDB-ConvLSTM

This paper proposes a fast video salient object detection model, based on a novel recurrent network architecture, named Pyramid Dilated Bidirectional ConvLSTM (PDB-ConvLSTM).

 Ranked #1 on Video Salient Object Detection on UVSD (using extra training data)

SEMANTIC SEGMENTATION UNSUPERVISED VIDEO OBJECT SEGMENTATION VIDEO SALIENT OBJECT DETECTION VIDEO SEMANTIC SEGMENTATION

MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object Segmentation

IEEE Transactions on Image Processing 2020 tfzhou/MATNet

To further demonstrate the generalization ability of our spatiotemporal learning framework, we extend MATNet to another relevant task: dynamic visual attention prediction (DVAP).

SEMANTIC SEGMENTATION UNSUPERVISED VIDEO OBJECT SEGMENTATION VIDEO SEMANTIC SEGMENTATION

Motion-Attentive Transition for Zero-Shot Video Object Segmentation

9 Mar 2020tfzhou/MATNet

In this paper, we present a novel Motion-Attentive Transition Network (MATNet) for zero-shot video object segmentation, which provides a new way of leveraging motion information to reinforce spatio-temporal object representation.

Ranked #2 on Unsupervised Video Object Segmentation on DAVIS 2016 (using extra training data)

SEMANTIC SEGMENTATION UNSUPERVISED VIDEO OBJECT SEGMENTATION VIDEO SEMANTIC SEGMENTATION

Learning Video Object Segmentation from Unlabeled Videos

CVPR 2020 carrierlxk/MuG

We propose a new method for video object segmentation (VOS) that addresses object pattern learning from unlabeled videos, unlike most existing methods which rely heavily on extensive annotated data.

REPRESENTATION LEARNING SEMANTIC SEGMENTATION SEMI-SUPERVISED VIDEO OBJECT SEGMENTATION UNSUPERVISED VIDEO OBJECT SEGMENTATION VIDEO SEMANTIC SEGMENTATION

Semi-Supervised Video Salient Object Detection Using Pseudo-Labels

ICCV 2019 Kinpzz/RCRNet-Pytorch

Specifically, we present an effective video saliency detector that consists of a spatial refinement network and a spatiotemporal module.

UNSUPERVISED VIDEO OBJECT SEGMENTATION VIDEO SALIENT OBJECT DETECTION