Unsupervised Video Object Segmentation Edit Task
Computer Vision • Video Object Segmentation
About
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
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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.
Ranked #1 on
Unsupervised Video Object Segmentation
on YouTube
SEMANTIC SEGMENTATION UNSUPERVISED VIDEO OBJECT SEGMENTATION VIDEO SEMANTIC SEGMENTATION
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.
Ranked #1 on
Youtube-VOS
on YouTube-VOS
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.
EYE TRACKING SEMANTIC SEGMENTATION UNSUPERVISED VIDEO OBJECT SEGMENTATION VIDEO SEGMENTATION VIDEO SEMANTIC SEGMENTATION
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.
Ranked #16 on
Unsupervised Video Object Segmentation
on DAVIS 2016
OPTICAL FLOW ESTIMATION SEMANTIC SEGMENTATION SEMI-SUPERVISED VIDEO OBJECT SEGMENTATION UNSUPERVISED VIDEO OBJECT SEGMENTATION VIDEO SEMANTIC SEGMENTATION VISUAL OBJECT TRACKING
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
• tfzhou/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
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)
SALIENT OBJECT DETECTION SEMANTIC SEGMENTATION UNSUPERVISED VIDEO OBJECT SEGMENTATION VIDEO SALIENT OBJECT DETECTION VIDEO SEMANTIC SEGMENTATION
Learning Video Object Segmentation from Unlabeled Videos
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
STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos
ECCV 2020
• sabarim/STEm-Seg
•
In this paper, we pro-pose a different approach that is well-suited to a variety of tasks involvinginstance segmentation in videos.
INSTANCE SEGMENTATION SEMANTIC SEGMENTATION UNSUPERVISED VIDEO OBJECT SEGMENTATION VIDEO INSTANCE SEGMENTATION