Unsupervised Video Object Segmentation
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Benchmarks
DAVIS 2016 val
YouTube-Objects
FBMS test
DAVIS 2017 (val)
DAVIS 2017 (test-dev)
SegTrack v2
Most implemented
EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency
Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos
MAST: A Memory-Augmented Self-supervised Tracker
Joint-task Self-supervised Learning for Temporal Correspondence
Papers
CMTM: Cross-Modal Token Modulation for Unsupervised Video Object Segmentation
Recent advances in unsupervised video object segmentation have highlighted the potential of two-stream architectures that integrate appearance and motion cues. However, fully leveraging these complementary sources of inf…
Unsupervised Video Object SegmentationLearning Object-Centric Representations Based on Slots in Real World Scenarios
A central goal in AI is to represent scenes as compositions of discrete objects, enabling fine-grained, controllable image and video generation. Yet leading diffusion models treat images holistically and rely on text con…
Unsupervised Video Object SegmentationVideo GenerationImage GenerationShallow Features Matter: Hierarchical Memory with Heterogeneous Interaction for Unsupervised Video Object Segmentation
Unsupervised Video Object Segmentation (UVOS) aims to predict pixel-level masks for the most salient objects in videos without any prior annotations. While memory mechanisms have been proven critical in various video seg…
Unsupervised Video Object SegmentationVideo Saliency DetectionVideo SegmentationDepthFlow: Exploiting Depth-Flow Structural Correlations for Unsupervised Video Object Segmentation
Unsupervised video object segmentation (VOS) aims to detect the most prominent object in a video. Recently, two-stream approaches that leverage both RGB images and optical flow have gained significant attention, but thei…
Unsupervised Video Object SegmentationSaliency-Motion Guided Trunk-Collateral Network for Unsupervised Video Object Segmentation
Recent mainstream unsupervised video object segmentation (UVOS) motion-appearance approaches use either the bi-encoder structure to separately encode motion and appearance features, or the uni-encoder structure for joint…
Optical Flow EstimationSalient Object DetectionSemantic SegmentationUnsupervised Video Object Segmentation+3Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation
In this paper, we address the challenges in unsupervised video object segmentation (UVOS) by proposing an efficient algorithm, termed MTNet, which concurrently exploits motion and temporal cues. Unlike previous methods t…
Objectobject-detectionObject DetectionSalient Object Detection+6