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
Latest papers
Associating Objects with Transformers for Video Object Segmentation
yoxu515/aot-benchmark
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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.
TransVOS: Video Object Segmentation with Transformers
Recently, Space-Time Memory Network (STM) based methods have achieved state-of-the-art performance in semi-supervised video object segmentation (VOS).
Efficient Regional Memory Network for Video Object Segmentation
hzxie/RMNet
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CVPR 2021
For the current query frame, the query regions are tracked and predicted based on the optical flow estimated from the previous frame.
Separable Structure Modeling for Semi-supervised Video Object Segmentation
li-plus/SSM-VOS
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Specifically, we first compute a pixel-wise similarity matrix by using representations of reference and target pixels and then select top-rank reference pixels for target pixel classification.
SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation
SST extracts per-pixel representations for each object in a video using sparse attention over spatiotemporal features.
Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation
HYOJINPARK/Reuse_VOS
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CVPR 2021
Current state-of-the-art approaches for Semi-supervised Video Object Segmentation (Semi-VOS) propagates information from previous frames to generate segmentation mask for the current frame.
Make One-Shot Video Object Segmentation Efficient Again
timmeinhardt/trackformer
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NeurIPS 2020
In the semi-supervised setting, the first mask of each object is provided at test time.
Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation
lyxok1/STM-Training
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NeurIPS 2020
In this paper, we address several inadequacies of current video object segmentation pipelines.
Collaborative Video Object Segmentation by Multi-Scale Foreground-Background Integration
z-x-yang/CFBI
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This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation.
Hybrid-S2S: Video Object Segmentation with Recurrent Networks and Correspondence Matching
In this work, we study an RNN-based architecture and address some of these issues by proposing a hybrid sequence-to-sequence architecture named HS2S, utilizing a dual mask propagation strategy that allows incorporating the information obtained from correspondence matching.
