Video Object Segmentation
240 papers with code • 9 benchmarks • 17 datasets
Video object segmentation is a binary labeling problem aiming to separate foreground object(s) from the background region of a video.
For leaderboards please refer to the different subtasks.
Benchmarks
These leaderboards are used to track progress in Video Object Segmentation
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Libraries
Use these libraries to find Video Object Segmentation models and implementationsDatasets
DAVIS
DAVIS 2017
DAVIS 2016
YouTube-VOS 2018
FBMS
SegTrack-v2
Referring Expressions for DAVIS 2016 & 2017
MOSE
BL30K
AVSBench
Subtasks
Latest papers
Towards Temporally Consistent Referring Video Object Segmentation
bo-miao/HTR
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Referring Video Object Segmentation (R-VOS) methods face challenges in maintaining consistent object segmentation due to temporal context variability and the presence of other visually similar objects.
Efficient Video Object Segmentation via Modulated Cross-Attention Memory
Recently, transformer-based approaches have shown promising results for semi-supervised video object segmentation.
PSALM: Pixelwise SegmentAtion with Large Multi-Modal Model
zamling/psalm
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PSALM is a powerful extension of the Large Multi-modal Model (LMM) to address the segmentation task challenges.
Exploring Pre-trained Text-to-Video Diffusion Models for Referring Video Object Segmentation
We hypothesize that the latent representation learned from a pretrained generative T2V model encapsulates rich semantics and coherent temporal correspondences, thereby naturally facilitating video understanding.
Video Object Segmentation with Dynamic Query Modulation
zht8506/qmvos
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Storing intermediate frame segmentations as memory for long-range context modeling, spatial-temporal memory-based methods have recently showcased impressive results in semi-supervised video object segmentation (SVOS).
VideoMAC: Video Masked Autoencoders Meet ConvNets
nust-machine-intelligence-laboratory/videomac
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In this paper, we propose a new approach termed as \textbf{VideoMAC}, which combines video masked autoencoders with resource-friendly ConvNets.
UniVS: Unified and Universal Video Segmentation with Prompts as Queries
minghanli/univs
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Despite the recent advances in unified image segmentation (IS), developing a unified video segmentation (VS) model remains a challenge.
Lester: rotoscope animation through video object segmentation and tracking
This article introduces Lester, a novel method to automatically synthetise retro-style 2D animations from videos.
Vivim: a Video Vision Mamba for Medical Video Object Segmentation
Traditional convolutional neural networks have a limited receptive field while transformer-based networks are mediocre in constructing long-term dependency from the perspective of computational complexity.
OMG-Seg: Is One Model Good Enough For All Segmentation?
lxtgh/omg-seg
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In this work, we address various segmentation tasks, each traditionally tackled by distinct or partially unified models.
