Video Semantic Segmentation
325 papers with code • 5 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in Video Semantic Segmentation
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Libraries
Use these libraries to find Video Semantic Segmentation models and implementations
Cityscapes
CamVid
SegTrack-v2
VSPW
MOSE
ODMS
LaRS
Most implemented papers
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.
Fast Video Object Segmentation by Reference-Guided Mask Propagation
seoungwugoh/RGMP
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CVPR 2018
We validate our method on four benchmark sets that cover single and multiple object segmentation.
Tukey-Inspired Video Object Segmentation
We investigate the problem of strictly unsupervised video object segmentation, i. e., the separation of a primary object from background in video without a user-provided object mask or any training on an annotated dataset.
Rethinking the Evaluation of Video Summaries
Video summarization is a technique to create a short skim of the original video while preserving the main stories/content.
Multigrid Predictive Filter Flow for Unsupervised Learning on Videos
aimerykong/predictive-filter-flow
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We introduce multigrid Predictive Filter Flow (mgPFF), a framework for unsupervised learning on videos.
RANet: Ranking Attention Network for Fast Video Object Segmentation
Specifically, to integrate the insights of matching based and propagation based methods, we employ an encoder-decoder framework to learn pixel-level similarity and segmentation in an end-to-end manner.
MAST: A Memory-Augmented Self-supervised Tracker
zlai0/MAST
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CVPR 2020
Recent interest in self-supervised dense tracking has yielded rapid progress, but performance still remains far from supervised methods.
Learning Fast and Robust Target Models for Video Object Segmentation
andr345/frtm-vos
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CVPR 2020
The target appearance model consists of a light-weight module, which is learned during the inference stage using fast optimization techniques to predict a coarse but robust target segmentation.
Collaborative Video Object Segmentation by Foreground-Background Integration
This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation.
