Video Object Segmentation
241 papers with code • 9 benchmarks • 17 datasets
Video object segmentation is a binary labeling problem aiming to separate foreground object(s) from the background region of a video.
For leaderboards please refer to the different subtasks.
Benchmarks
These leaderboards are used to track progress in Video Object Segmentation
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Libraries
Use these libraries to find Video Object Segmentation models and implementationsDatasets
DAVIS
DAVIS 2017
DAVIS 2016
YouTube-VOS 2018
FBMS
SegTrack-v2
Referring Expressions for DAVIS 2016 & 2017
MOSE
BL30K
AVSBench
Subtasks
Most implemented papers
Video Object Segmentation with Re-identification
lxx1991/VS-ReID
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Specifically, our Video Object Segmentation with Re-identification (VS-ReID) model includes a mask propagation module and a ReID module.
Fast Online Object Tracking and Segmentation: A Unifying Approach
foolwood/SiamMask
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CVPR 2019
In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach.
FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation
tensorflow/models
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CVPR 2019
Many of the recent successful methods for video object segmentation (VOS) are overly complicated, heavily rely on fine-tuning on the first frame, and/or are slow, and are hence of limited practical use.
Video Object Segmentation using Space-Time Memory Networks
In our framework, the past frames with object masks form an external memory, and the current frame as the query is segmented using the mask information in the memory.
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.
Physarum Powered Differentiable Linear Programming Layers and Applications
HeatherJiaZG/SuperGlue-pytorch
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We describe our development and show the use of our solver in a video segmentation task and meta-learning for few-shot learning.
Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation
hkchengrex/STCN
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NeurIPS 2021
This paper presents a simple yet effective approach to modeling space-time correspondences in the context of video object segmentation.
EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations
VISOR annotates videos from EPIC-KITCHENS, which comes with a new set of challenges not encountered in current video segmentation datasets.
Learning Video Object Segmentation from Static Images
omkar13/MaskTrack
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CVPR 2017
Inspired by recent advances of deep learning in instance segmentation and object tracking, we introduce video object segmentation problem as a concept of guided instance segmentation.
Deep Extreme Cut: From Extreme Points to Object Segmentation
scaelles/DEXTR-PyTorch
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CVPR 2018
This paper explores the use of extreme points in an object (left-most, right-most, top, bottom pixels) as input to obtain precise object segmentation for images and videos.
