Video Object Segmentation
242 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
Emerging Properties in Self-Supervised Vision Transformers
facebookresearch/dino
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ICCV 2021
In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets).
One-Shot Video Object Segmentation
kmaninis/OSVOS-PyTorch
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CVPR 2017
This paper tackles the task of semi-supervised video object segmentation, i. e., the separation of an object from the background in a video, given the mask of the first frame.
PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation
We address semi-supervised video object segmentation, the task of automatically generating accurate and consistent pixel masks for objects in a video sequence, given the first-frame ground truth annotations.
Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion
hkchengrex/MiVOS
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CVPR 2021
We present Modular interactive VOS (MiVOS) framework which decouples interaction-to-mask and mask propagation, allowing for higher generalizability and better performance.
Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective
To learn generalizable representation for correspondence in large-scale, a variety of self-supervised pretext tasks are proposed to explicitly perform object-level or patch-level similarity learning.
Lucid Data Dreaming for Video Object Segmentation
Our approach is suitable for both single and multiple object segmentation.
YouTube-VOS: Sequence-to-Sequence Video Object Segmentation
BehradToghi/ECCV_Youtube_VOS
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ECCV 2018
End-to-end sequential learning to explore spatial-temporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i. e., even the largest video segmentation dataset only contains 90 short video clips.
Interactive Video Object Segmentation Using Global and Local Transfer Modules
yuk6heo/IVOS-ATNet
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ECCV 2020
The global transfer module conveys the segmentation information in an annotated frame to a target frame, while the local transfer module propagates the segmentation information in a temporally adjacent frame to the target frame.
Make One-Shot Video Object Segmentation Efficient Again
dvl-tum/e-osvos
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NeurIPS 2020
In the semi-supervised setting, the first mask of each object is provided at test time.
Video Polyp Segmentation: A Deep Learning Perspective
We present the first comprehensive video polyp segmentation (VPS) study in the deep learning era.
