Papers Semi-Supervised Video Object Segmentation
“Semi-Supervised Video Object Segmentation” 태그가 달린 논문 154편 · 필터 해제
FlowVOS: Weakly-Supervised Visual Warping for Detail-Preserving and Temporally Consistent Single-Shot Video Object Segmentation
We consider the task of semi-supervised video object segmentation (VOS). Our approach mitigates shortcomings in previous VOS work by addressing detail preservation and temporal consistency using visual warping. In contra…
ObjectOptical Flow EstimationSegmentationSemantic Segmentation+3FAMINet: Learning Real-time Semi-supervised Video Object Segmentation with Steepest Optimized Optical Flow
Semi-supervised video object segmentation (VOS) aims to segment a few moving objects in a video sequence, where these objects are specified by annotation of first frame. The optical flow has been considered in many exist…
Optical Flow EstimationSegmentationSemantic SegmentationSemi-Supervised Video Object Segmentation+2Dense 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+3Exploring the Semi-supervised Video Object Segmentation Problem from a Cyclic Perspective
Modern video object segmentation (VOS) algorithms have achieved remarkably high performance in a sequential processing order, while most of currently prevailing pipelines still show some obvious inadequacy like accumulat…
SegmentationSemantic SegmentationSemi-Supervised Video Object SegmentationVideo Object Segmentation+1Pixel-Level Bijective Matching for Video Object Segmentation
Semi-supervised video object segmentation (VOS) aims to track the designated objects present in the initial frame of a video at the pixel level. To fully exploit the appearance information of an object, pixel-level featu…
ObjectSemantic SegmentationSemi-Supervised Video Object SegmentationVideo Object Segmentation+1Hierarchical Memory Matching Network for Video Object Segmentation
We present Hierarchical Memory Matching Network (HMMN) for semi-supervised video object segmentation. Based on a recent memory-based method [33], we propose two advanced memory read modules that enable us to perform memo…
ObjectRetrievalSemantic SegmentationSemi-Supervised Video Object Segmentation+2Joint Inductive and Transductive Learning for Video Object Segmentation
Semi-supervised video object segmentation is a task of segmenting the target object in a video sequence given only a mask annotation in the first frame. The limited information available makes it an extremely challenging…
Inductive LearningObjectSemantic SegmentationSemi-Supervised Video Object Segmentation+3Self-Supervised Video Object Segmentation by Motion-Aware Mask Propagation
We propose a self-supervised spatio-temporal matching method, coined Motion-Aware Mask Propagation (MAMP), for video object segmentation. MAMP leverages the frame reconstruction task for training without the need for ann…
SegmentationSemantic SegmentationSemi-Supervised Video Object SegmentationVideo Object Segmentation+1Accelerating Video Object Segmentation with Compressed Video
We propose an efficient plug-and-play acceleration framework for semi-supervised video object segmentation by exploiting the temporal redundancies in videos presented by the compressed bitstream. Specifically, we propose…
ObjectSegmentationSemantic SegmentationSemi-Supervised Video Object Segmentation+2Do Different Tracking Tasks Require Different Appearance Models?
Tracking objects of interest in a video is one of the most popular and widely applicable problems in computer vision. However, with the years, a Cambrian explosion of use cases and benchmarks has fragmented the problem i…
Multi-Object TrackingMulti-Object Tracking and SegmentationMultiple People TrackingObject Tracking+10Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation
This paper presents a simple yet effective approach to modeling space-time correspondences in the context of video object segmentation. Unlike most existing approaches, we establish correspondences directly between frame…
Semantic SegmentationSemi-Supervised Video Object SegmentationVideo Object SegmentationVideo Semantic SegmentationAssociating Objects with Transformers for Video Object Segmentation
This paper investigates how to realize better and more efficient embedding learning to tackle the semi-supervised video object segmentation under challenging multi-object scenarios. The state-of-the-art methods learn to …
ObjectOne-shot visual object segmentationSemantic SegmentationSemi-Supervised Video Object Segmentation+3TransVOS: Video Object Segmentation with Transformers
Recently, Space-Time Memory Network (STM) based methods have achieved state-of-the-art performance in semi-supervised video object segmentation (VOS). A crucial problem in this task is how to model the dependency both am…
ObjectOne-shot visual object segmentationSegmentationSemantic Segmentation+3DAVOS: Semi-Supervised Video Object Segmentation via Adversarial Domain Adaptation
Domain shift has always been one of the primary issues in video object segmentation (VOS), for which models suffer from degeneration when tested on unfamiliar datasets. Recently, many online methods have emerged to narro…
Domain AdaptationSemantic SegmentationSemi-Supervised Video Object SegmentationVideo Object Segmentation+1Learning Position and Target Consistency for Memory-based Video Object Segmentation
This paper studies the problem of semi-supervised video object segmentation(VOS). Multiple works have shown that memory-based approaches can be effective for video object segmentation. They are mostly based on pixel-leve…
ObjectOne-shot visual object segmentationPositionSegmentation+4Efficient Regional Memory Network for Video Object Segmentation
Recently, several Space-Time Memory based networks have shown that the object cues (e.g. video frames as well as the segmented object masks) from the past frames are useful for segmenting objects in the current frame. Ho…
ObjectOne-shot visual object segmentationOptical Flow EstimationSemantic Segmentation+3Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion
We present Modular interactive VOS (MiVOS) framework which decouples interaction-to-mask and mask propagation, allowing for higher generalizability and better performance. Trained separately, the interaction module conve…
Interactive Video Object SegmentationSemantic SegmentationSemi-Supervised Video Object SegmentationVideo Object Segmentation+1Separable Structure Modeling for Semi-supervised Video Object Segmentation
In this paper, we propose a separable structure modeling approach for semi-supervised video object segmentation. Unlike most existing methods which preclude the semantically structural information of target objects, our…
ObjectOne-shot visual object segmentationSemi-Supervised Video Object SegmentationVideo Object Segmentation+1SwiftNet: Real-time Video Object Segmentation
In this work we present SwiftNet for real-time semisupervised video object segmentation (one-shot VOS), which reports 77.8% J &F and 70 FPS on DAVIS 2017 validation dataset, leading all present solutions in overall accur…
ObjectSegmentationSemantic SegmentationSemi-Supervised Video Object Segmentation+2SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation
In this paper we introduce a Transformer-based approach to video object segmentation (VOS). To address compounding error and scalability issues of prior work, we propose a scalable, end-to-end method for VOS called Spars…
Inductive BiasMotion SegmentationObjectOne-shot visual object segmentation+6