One-shot visual object segmentation
26 papers with code • 2 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in One-shot visual object segmentation
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YouTube-VOS 2018
Most implemented papers
PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation
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
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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.
Make One-Shot Video Object Segmentation Efficient Again
dvl-tum/e-osvos
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NeurIPS 2020
In the semi-supervised setting, the first mask of each object is provided at test time.
Video Object Segmentation using Space-Time Memory Networks
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.
Deep Flow-Guided Video Inpainting
nbei/Deep-Flow-Guided-Video-Inpainting
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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
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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
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
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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
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
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CVPR 2019
One of the fundamental challenges in video object segmentation is to find an effective representation of the target and background appearance.
