Papers Unsupervised Video Object Segmentation
“Unsupervised Video Object Segmentation” 태그가 달린 논문 93편 · 필터 해제
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+6Improving Unsupervised Video Object Segmentation via Fake Flow Generation
Unsupervised video object segmentation (VOS), also known as video salient object detection, aims to detect the most prominent object in a video at the pixel level. Recently, two-stream approaches that leverage both RGB i…
Objectobject-detectionObject DetectionOptical Flow Estimation+6Self-supervised Video Object Segmentation with Distillation Learning of Deformable Attention
Video object segmentation is a fundamental research problem in computer vision. Recent techniques have often applied attention mechanism to object representation learning from video sequences. However, due to temporal ch…
Knowledge DistillationObjectRepresentation LearningSegmentation+4SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation
Unsupervised video object segmentation (UVOS) aims at detecting the primary objects in a given video sequence without any human interposing. Most existing methods rely on two-stream architectures that separately encode t…
Objectobject-detectionObject DetectionOptical Flow Estimation+6Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
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. To leverage the property that salient objects usually have distinc…
ObjectOptical Flow EstimationSemantic SegmentationUnsupervised Video Object Segmentation+2Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation
Unsupervised Video Object Segmentation (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge. However, previous methods do not fully use spatial-temporal context and f…
Semantic SegmentationUnsupervised Video Object SegmentationVideo Object SegmentationVideo Semantic SegmentationTracking Anything with Decoupled Video Segmentation
Training data for video segmentation are expensive to annotate. This impedes extensions of end-to-end algorithms to new video segmentation tasks, especially in large-vocabulary settings. To 'track anything' without train…
Open-Vocabulary Video SegmentationOpen-World Video SegmentationPanoptic SegmentationReferring Expression Segmentation+9Online 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. A major chall…
ClusteringContrastive LearningObjectOptical Flow Estimation+5UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything Model
The current state-of-the-art methods for unsupervised video object segmentation (UVOS) require extensive training on video datasets with mask annotations, limiting their effectiveness in handling challenging scenarios. H…
Image SegmentationObjectObject TrackingSegmentation+4Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual Grouping
We study learning object segmentation from unlabeled videos. Humans can easily segment moving objects without knowing what they are. The Gestalt law of common fate, i.e., what move at the same speed belong together, has …
Motion SegmentationObjectObject DiscoveryOptical Flow Estimation+6Adaptive Multi-source Predictor for Zero-shot Video Object Segmentation
Static and moving objects often occur in real-life videos. Most video object segmentation methods only focus on extracting and exploiting motion cues to perceive moving objects. Once faced with the frames of static objec…
ObjectOptical Flow EstimationSemantic SegmentationUnsupervised Video Object Segmentation+3Guided Slot Attention for Unsupervised Video Object Segmentation
Unsupervised video object segmentation aims to segment the most prominent object in a video sequence. However, the existence of complex backgrounds and multiple foreground objects make this task challenging. To address t…
ObjectSemantic SegmentationUnsupervised Video Object SegmentationVideo Object Segmentation+1Tsanet: Temporal and Scale Alignment for Unsupervised Video Object Segmentation
Unsupervised Video Object Segmentation (UVOS) refers to the challenging task of segmenting the prominent object in videos without manual guidance. In recent works, two approaches for UVOS have been discussed that can be …
DecoderObjectOptical Flow EstimationSemantic Segmentation+3Maximal Cliques on Multi-Frame Proposal Graph for Unsupervised Video Object Segmentation
Unsupervised Video Object Segmentation (UVOS) aims at discovering objects and tracking them through videos. For accurate UVOS, we observe if one can locate precise segment proposals on key frames, subsequent processes ar…
Instance SegmentationObjectSegmentationSemantic Segmentation+4Flow-guided Semi-supervised Video Object Segmentation
We propose an optical flow-guided approach for semi-supervised video object segmentation. Optical flow is usually exploited as additional guidance information in unsupervised video object segmentation. However, its relev…
DecoderObjectOptical Flow EstimationSegmentation+5