Semi-Supervised Video Object Segmentation

48 papers with code • 5 benchmarks • 5 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.

Greatest papers with code

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.

Semantic Segmentation Semi-Supervised Video Object Segmentation +1

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.

Real-Time Visual Tracking Semi-Supervised Semantic Segmentation +2

State-Aware Tracker for Real-Time Video Object Segmentation

MegviiDetection/video_analyst CVPR 2020

For higher efficiency, SAT takes advantage of the inter-frame consistency and deals with each target object as a tracklet.

Semantic Segmentation Semi-Supervised Video Object Segmentation +1

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.

Semi-Supervised Video Object Segmentation Video Segmentation +1

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.

Ranked #4 on Interactive Video Object Segmentation on DAVIS 2017 (using extra training data)

Interactive Video Object Segmentation One-shot visual object segmentation +2

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.

Semantic Segmentation Semi-Supervised Video Object Segmentation +1

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.

Semantic Segmentation Semi-Supervised Video Object Segmentation +1