Browse > Computer Vision > Video Object Segmentation > Semi-supervised Video Object Segmentation

Semi-supervised Video Object Segmentation

7 papers with code · Computer Vision

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

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Greatest papers with code

Fast Online Object Tracking and Segmentation: A Unifying Approach

CVPR 2019 foolwood/SiamMask

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 SEMI-SUPERVISED VIDEO OBJECT SEGMENTATION VISUAL OBJECT TRACKING

One-Shot Video Object Segmentation

CVPR 2017 scaelles/OSVOS-TensorFlow

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

RVOS: End-to-End Recurrent Network for Video Object Segmentation

CVPR 2019 imatge-upc/rvos

Multiple object video object segmentation is a challenging task, specially for the zero-shot case, when no object mask is given at the initial frame and the model has to find the objects to be segmented along the sequence.

SEMI-SUPERVISED VIDEO OBJECT SEGMENTATION UNSUPERVISED VIDEO OBJECT SEGMENTATION

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

24 Jul 2018JonathonLuiten/PReMVOS

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.

SEMANTIC SEGMENTATION SEMI-SUPERVISED VIDEO OBJECT SEGMENTATION VIDEO SEMANTIC SEGMENTATION

MHP-VOS: Multiple Hypotheses Propagation for Video Object Segmentation

CVPR 2019 shuangjiexu/MHP-VOS

Extensive experiments on challenging datasets demonstrate the effectiveness of the proposed method, especially in the case of object missing.

DECISION MAKING SEMANTIC SEGMENTATION SEMI-SUPERVISED VIDEO OBJECT SEGMENTATION VIDEO SEMANTIC SEGMENTATION