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
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.
Flexible visual prompts for in-context learning in computer vision
Additionally, we propose a technique for support set selection, which involves choosing the most relevant images to include in this set.
Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation
In this paper, we propose a simple yet effective approach for self-supervised video object segmentation (VOS).
SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation
In-context segmentation aims at segmenting novel images using a few labeled example images, termed as "in-context examples", exploring content similarities between examples and the target.
Putting the Object Back into Video Object Segmentation
hkchengrex/Cutie
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We present Cutie, a video object segmentation (VOS) network with object-level memory reading, which puts the object representation from memory back into the video object segmentation result.
