Semi-Supervised Video Object Segmentation

69 papers with code • 12 benchmarks • 6 datasets

The semi-supervised scenario assumes the user inputs a full mask of the object(s) of interest in the first frame of a video sequence. Methods have to produce the segmentation mask for that object(s) in the subsequent frames.


Use these libraries to find Semi-Supervised Video Object Segmentation models and implementations

Most implemented papers

PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation

JonathonLuiten/PReMVOS 24 Jul 2018

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 CVPR 2021

We present Modular interactive VOS (MiVOS) framework which decouples interaction-to-mask and mask propagation, allowing for higher generalizability and better performance.

Lucid Data Dreaming for Video Object Segmentation

omkar13/MaskTrack 28 Mar 2017

Our approach is suitable for both single and multiple object segmentation.

YouTube-VOS: Sequence-to-Sequence Video Object Segmentation

BehradToghi/ECCV_Youtube_VOS 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.

One-Shot Video Object Segmentation

kmaninis/OSVOS-PyTorch 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.

Fast Online Object Tracking and Segmentation: A Unifying Approach

foolwood/SiamMask 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 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

seoungwugoh/STM ICCV 2019

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.

Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation

hkchengrex/STCN NeurIPS 2021

This paper presents a simple yet effective approach to modeling space-time correspondences in the context of video object segmentation.

Learning Video Object Segmentation from Static Images

omkar13/MaskTrack 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.