Papers Unsupervised Video Object Segmentation
“Unsupervised Video Object Segmentation” 태그가 달린 논문 93편 · 필터 해제
Unsupervised Video Object Segmentation with Online Adversarial Self-Tuning
The existing unsupervised video object segmentation methods depend heavily on the segmentation model trained offline on a labeled training video set, and cannot well generalize to the test videos from a different dom…
ObjectPseudo LabelSegmentationSemantic Segmentation+3Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy
We present IMAS, a method that segments the primary objects in videos without manual annotation in training or inference. Previous methods in unsupervised video object segmentation (UVOS) have demonstrated the effectiven…
MisconceptionsObjectObject DiscoverySegmentation+4Dual Prototype Attention for Unsupervised Video Object Segmentation
Unsupervised video object segmentation (VOS) aims to detect and segment the most salient object in videos. The primary techniques used in unsupervised VOS are 1) the collaboration of appearance and motion information; an…
ObjectSemantic SegmentationUnsupervised Video Object SegmentationVideo Object Segmentation+1Efficient Unsupervised Video Object Segmentation Network Based on Motion Guidance
Due to the problem of performance constraints of unsupervised video object detection, its large-scale application is limited. In response to this pain point, we propose another excellent method to solve this problematic …
object-detectionObject DetectionOptical Flow EstimationSemantic Segmentation+4A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation
We propose a simple, yet powerful approach for unsupervised object segmentation in videos. We introduce an objective function whose minimum represents the mask of the main salient object over the input sequence. It only …
Clusteringglobal-optimizationObjectSemantic Segmentation+5Unsupervised Video Object Segmentation via Prototype Memory Network
Unsupervised video object segmentation aims to segment a target object in the video without a ground truth mask in the initial frame. This challenging task requires extracting features for the most salient common objects…
ObjectOptical Flow EstimationSelf-LearningSemantic Segmentation+3Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
Unsupervised video object segmentation (VOS) aims to detect the most salient object in a video sequence at the pixel level. In unsupervised VOS, most state-of-the-art methods leverage motion cues obtained from optical fl…
Optical Flow EstimationSemantic SegmentationUnsupervised Video Object SegmentationVideo Object Segmentation+1TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut
In this paper, we describe a graph-based algorithm that uses the features obtained by a self-supervised transformer to detect and segment salient objects in images and videos. With this approach, the image patches that c…
Object DiscoverySaliency DetectionSegmentationSemantic Segmentation+6Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation
Optical flow is an easily conceived and precious cue for advancing unsupervised video object segmentation (UVOS). Most of the previous methods directly extract and fuse the motion and appearance features for segmenting t…
ObjectOptical Flow EstimationSemantic SegmentationUnsupervised Video Object Segmentation+3Implicit Motion-Compensated Network for Unsupervised Video Object Segmentation
Unsupervised video object segmentation (UVOS) aims at automatically separating the primary foreground object(s) from the background in a video sequence. Existing UVOS methods either lack robustness when there are visuall…
Motion CompensationSemantic SegmentationUnsupervised Video Object SegmentationVideo Object Segmentation+1In-N-Out Generative Learning for Dense Unsupervised Video Segmentation
In this paper, we focus on unsupervised learning for Video Object Segmentation (VOS) which learns visual correspondence (i.e., the similarity between pixel-level features) from unlabeled videos. Previous methods are main…
Contrastive LearningSemantic SegmentationUnsupervised Video Object SegmentationVideo Object Segmentation+2Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier
Unsupervised video object segmentation (UVOS) is a per-pixel binary labeling problem which aims at separating the foreground object from the background in the video without using the ground truth (GT) mask of the foregro…
ObjectSemantic SegmentationUnsupervised Video Object SegmentationVideo Object Segmentation+1Autoencoder-based background reconstruction and foreground segmentation with background noise estimation
Even after decades of research, dynamic scene background reconstruction and foreground object segmentation are still considered as open problems due various challenges such as illumination changes, camera movements, or b…
Foreground SegmentationSegmentationUnsupervised Video Object SegmentationVideo Background Subtraction+1Learning To Segment Dominant Object Motion From Watching Videos
Existing deep learning based unsupervised video object segmentation methods still rely on ground-truth segmentation masks to train. Unsupervised in this context only means that no annotated frames are used during inferen…
ObjectOptical Flow EstimationSegmentationSemantic Segmentation+3D^2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos
Despite receiving significant attention from the research community, the task of segmenting and tracking objects in monocular videos still has much room for improvement. Existing works have simultaneously justified the e…
SegmentationSemantic SegmentationUnsupervised Video Object SegmentationVideo Object Segmentation+2D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos
Despite receiving significant attention from the research community, the task of segmenting and tracking objects in monocular videos still has much room for improvement. Existing works have simultaneously justified the e…
Multi-Object Tracking and SegmentationSegmentationSemantic SegmentationUnsupervised Video Object Segmentation+4Dense Unsupervised Learning for Video Segmentation
We present a novel approach to unsupervised learning for video object segmentation (VOS). Unlike previous work, our formulation allows to learn dense feature representations directly in a fully convolutional regime. We r…
SegmentationSemantic SegmentationSemi-Supervised Video Object SegmentationUnsupervised Video Object Segmentation+3Video Salient Object Detection via Contrastive Features and Attention Modules
Video salient object detection aims to find the most visually distinctive objects in a video. To explore the temporal dependencies, existing methods usually resort to recurrent neural networks or optical flow. However, t…
Contrastive LearningObjectobject-detectionObject Detection+7Multi-Source Fusion and Automatic Predictor Selection for Zero-Shot Video Object Segmentation
Location and appearance are the key cues for video object segmentation. Many sources such as RGB, depth, optical flow and static saliency can provide useful information about the objects. However, existing approaches onl…
Depth EstimationObjectSalient Object DetectionUnsupervised Video Object Segmentation+2Full-Duplex Strategy for Video Object Segmentation
Previous video object segmentation approaches mainly focus on using simplex solutions between appearance and motion, limiting feature collaboration efficiency among and across these two cues. In this work, we study a nov…
ObjectObject DetectionSalient Object DetectionSegmentation+6