Unsupervised Video Object Segmentation
51 papers with code • 6 benchmarks • 8 datasets
The unsupervised scenario assumes that the user does not interact with the algorithm to obtain the segmentation masks. Methods should provide a set of object candidates with no overlapping pixels that span through the whole video sequence. This set of objects should contain at least the objects that capture human attention when watching the whole video sequence i.e objects that are more likely to be followed by human gaze.
Benchmarks
These leaderboards are used to track progress in Unsupervised Video Object Segmentation
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Datasets
DAVIS 2017
DAVIS 2016
FBMS
SegTrack-v2
Referring Expressions for DAVIS 2016 & 2017
MOSE
FBMS-59
BL30K
Latest papers
Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
suhwan-cho/tmo
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Unsupervised video object segmentation (VOS) is a task that aims to detect the most salient object in a video without external guidance about the object.
Tracking Anything with Decoupled Video Segmentation
hkchengrex/Tracking-Anything-with-DEVA
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ICCV 2023
To 'track anything' without training on video data for every individual task, we develop a decoupled video segmentation approach (DEVA), composed of task-specific image-level segmentation and class/task-agnostic bi-directional temporal propagation.
Online Unsupervised Video Object Segmentation via Contrastive Motion Clustering
Online unsupervised video object segmentation (UVOS) uses the previous frames as its input to automatically separate the primary object(s) from a streaming video without using any further manual annotation.
Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual Grouping
TonyLianLong/RCF-UnsupVideoSeg
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CVPR 2023
The Gestalt law of common fate, i. e., what move at the same speed belong together, has inspired unsupervised object discovery based on motion segmentation.
Adaptive Multi-source Predictor for Zero-shot Video Object Segmentation
xiaoqi-zhao-dlut/multi-source-aps-zvos
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In the static object predictor, the RGB source is converted to depth and static saliency sources, simultaneously.
Guided Slot Attention for Unsupervised Video Object Segmentation
Unsupervised video object segmentation aims to segment the most prominent object in a video sequence.
Dual Prototype Attention for Unsupervised Video Object Segmentation
hydragon516/dpa
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Unsupervised video object segmentation (VOS) aims to detect and segment the most salient object in videos.
A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation
We propose a simple, yet powerful approach for unsupervised object segmentation in videos.
Unsupervised Video Object Segmentation via Prototype Memory Network
Hydragon516/PMN
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The proposed model effectively extracts the RGB and motion information by extracting superpixel-based component prototypes from the input RGB images and optical flow maps.
Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
suhwan-cho/tmo
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Unsupervised video object segmentation (VOS) aims to detect the most salient object in a video sequence at the pixel level.
