Video Object Segmentation
243 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
LVOS
Subtasks
Latest papers
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.
1st Place Solution for 5th LSVOS Challenge: Referring Video Object Segmentation
robertluo1/iccv2023_rvos_challenge
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The recent transformer-based models have dominated the Referring Video Object Segmentation (RVOS) task due to the superior performance.
Tracking with Human-Intent Reasoning
The perception component then generates the tracking results based on the embeddings.
UniRef++: Segment Every Reference Object in Spatial and Temporal Spaces
We evaluate our unified models on various benchmarks.
Hierarchical Graph Pattern Understanding for Zero-Shot VOS
nust-machine-intelligence-laboratory/hgpu
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However, existing optical flow-based methods have a significant dependency on optical flow, which results in poor performance when the optical flow estimation fails for a particular scene.
General Object Foundation Model for Images and Videos at Scale
We present GLEE in this work, an object-level foundation model for locating and identifying objects in images and videos.
Semi-supervised Active Learning for Video Action Detection
akash2907/semi-sup-active-learning
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First, we demonstrate its effectiveness on video action detection where the proposed approach outperforms prior works in semi-supervised and weakly-supervised learning along with several baseline approaches in both UCF101-24 and JHMDB-21.
