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Unsupervised Video Object Segmentation
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation
Improving Unsupervised Video Object Segmentation via Fake Flow Generation
Self-supervised Video Object Segmentation with Distillation Learning of Deformable Attention
SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation
Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation
Tracking Anything with Decoupled Video Segmentation
Online Unsupervised Video Object Segmentation via Contrastive Motion Clustering
UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything Model
Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual Grouping
Adaptive Multi-source Predictor for Zero-shot Video Object Segmentation
Guided Slot Attention for Unsupervised Video Object Segmentation
Tsanet: Temporal and Scale Alignment for Unsupervised Video Object Segmentation
Maximal Cliques on Multi-Frame Proposal Graph for Unsupervised Video Object Segmentation
Flow-guided Semi-supervised Video Object Segmentation
Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy
Dual Prototype Attention for Unsupervised Video Object Segmentation
Efficient Unsupervised Video Object Segmentation Network Based on Motion Guidance
A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation
Unsupervised Video Object Segmentation via Prototype Memory Network
Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut
Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation
Implicit Motion-Compensated Network for Unsupervised Video Object Segmentation
In-N-Out Generative Learning for Dense Unsupervised Video Segmentation
Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier
Autoencoder-based background reconstruction and foreground segmentation with background noise estimation
Learning To Segment Dominant Object Motion From Watching Videos
D^2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos
Dense Unsupervised Learning for Video Segmentation
Video Salient Object Detection via Contrastive Features and Attention Modules
Multi-Source Fusion and Automatic Predictor Selection for Zero-Shot Video Object Segmentation
Full-Duplex Strategy for Video Object Segmentation
Video Instance Segmentation with a Propose-Reduce Paradigm
F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation
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