Semi-Supervised Video Object Segmentation

94 papers with code • 15 benchmarks • 13 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.

Libraries

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

Most implemented papers

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.

Fast Video Object Segmentation by Reference-Guided Mask Propagation

seoungwugoh/RGMP CVPR 2018

We validate our method on four benchmark sets that cover single and multiple object segmentation.

RANet: Ranking Attention Network for Fast Video Object Segmentation

Storife/RANet ICCV 2019

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.

Joint-task Self-supervised Learning for Temporal Correspondence

Liusifei/UVC NeurIPS 2019

Our learning process integrates two highly related tasks: tracking large image regions \emph{and} establishing fine-grained pixel-level associations between consecutive video frames.

MAST: A Memory-Augmented Self-supervised Tracker

zlai0/MAST 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 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

z-x-yang/CFBI ECCV 2020

This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation.

Learning What to Learn for Video Object Segmentation

visionml/pytracking ECCV 2020

This allows us to achieve a rich internal representation of the target in the current frame, significantly increasing the segmentation accuracy of our approach.

Associating Objects with Transformers for Video Object Segmentation

z-x-yang/AOT NeurIPS 2021

The state-of-the-art methods learn to decode features with a single positive object and thus have to match and segment each target separately under multi-object scenarios, consuming multiple times computing resources.