One-shot visual object segmentation

26 papers with code • 2 benchmarks • 1 datasets

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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.

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.

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.

Make One-Shot Video Object Segmentation Efficient Again

dvl-tum/e-osvos NeurIPS 2020

In the semi-supervised setting, the first mask of each object is provided at test time.

Deep Flow-Guided Video Inpainting

nbei/Deep-Flow-Guided-Video-Inpainting CVPR 2019

Then the synthesized flow field is used to guide the propagation of pixels to fill up the missing regions in the video.

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.

A Generative Appearance Model for End-to-end Video Object Segmentation

joakimjohnander/agame-vos CVPR 2019

One of the fundamental challenges in video object segmentation is to find an effective representation of the target and background appearance.