Papers Semi-Supervised Video Object Segmentation
“Semi-Supervised Video Object Segmentation” 태그가 달린 논문 154편 · 필터 해제
State-Aware Tracker for Real-Time Video Object Segmentation
In this work, we address the task of semi-supervised video object segmentation(VOS) and explore how to make efficient use of video property to tackle the challenge of semi-supervision. We propose a novel pipeline called …
SegmentationSemantic SegmentationSemi-Supervised Video Object SegmentationVideo Object Segmentation+1Learning Fast and Robust Target Models for Video Object Segmentation
Video object segmentation (VOS) is a highly challenging problem since the initial mask, defining the target object, is only given at test-time. The main difficulty is to effectively handle appearance changes and similar …
One-shot visual object segmentationSegmentationSemantic SegmentationSemi-Supervised Video Object Segmentation+2MAST: A Memory-Augmented Self-supervised Tracker
Recent interest in self-supervised dense tracking has yielded rapid progress, but performance still remains far from supervised methods. We propose a dense tracking model trained on videos without any annotations that su…
Semantic SegmentationSemi-Supervised Video Object SegmentationUnsupervised Video Object SegmentationVideo Object Segmentation+1Directional Deep Embedding and Appearance Learning for Fast Video Object Segmentation
Most recent semi-supervised video object segmentation (VOS) methods rely on fine-tuning deep convolutional neural networks online using the given mask of the first frame or predicted masks of subsequent frames. However, …
GPUOne-shot visual object segmentationSegmentationSemantic Segmentation+3CRVOS: Clue Refining Network for Video Object Segmentation
The encoder-decoder based methods for semi-supervised video object segmentation (Semi-VOS) have received extensive attention due to their superior performances. However, most of them have complex intermediate networks wh…
DecoderObjectSegmentationSemantic Segmentation+4Fast Video Object Segmentation using the Global Context Module
We developed a real-time, high-quality semi-supervised video object segmentation algorithm. Its accuracy is on par with the most accurate, time-consuming online-learning model, while its speed is similar to the fastest t…
ObjectSegmentationSemantic SegmentationSemi-Supervised Video Object Segmentation+3UnOVOST: Unsupervised Offline Video Object Segmentation and Tracking
We address Unsupervised Video Object Segmentation (UVOS), the task of automatically generating accurate pixel masks for salient objects in a video sequence and of tracking these objects consistently through time, without…
ObjectSegmentationSemantic SegmentationSemi-Supervised Video Object Segmentation+3Siam R-CNN: Visual Tracking by Re-Detection
We present Siam R-CNN, a Siamese re-detection architecture which unleashes the full power of two-stage object detection approaches for visual object tracking. We combine this with a novel tracklet-based dynamic programmi…
Objectobject-detectionObject DetectionObject Tracking+3AGSS-VOS: Attention Guided Single-Shot Video Object Segmentation
Most video object segmentation approaches process objects separately. This incurs high computational cost when multiple objects exist. In this paper, we propose AGSS-VOS to segment multiple objects in one feed-forward pa…
DecoderObjectOne-shot visual object segmentationSemantic Segmentation+3Fast Video Object Segmentation via Dynamic Targeting Network
We propose a new model for fast and accurate video object segmentation. It consists of two convolutional neural networks, a Dynamic Targeting Network (DTN) and a Mask Refinement Network (MRN). DTN locates the object by d…
ObjectSegmentationSemantic SegmentationSemi-Supervised Video Object Segmentation+2Towards Good Practices for Video Object Segmentation
Semi-supervised video object segmentation is an interesting yet challenging task in machine learning. In this work, we conduct a series of refinements with the propagation-based video object segmentation method and empir…
BIG-bench Machine LearningObjectOne-shot visual object segmentationSegmentation+4CapsuleVOS: Semi-Supervised Video Object Segmentation Using Capsule Routing
In this work we propose a capsule-based approach for semi-supervised video object segmentation. Current video object segmentation methods are frame-based and often require optical flow to capture temporal consistency acr…
ObjectOne-shot visual object segmentationOptical Flow EstimationSegmentation+5Adaptive ROI Generation for Video Object Segmentation Using Reinforcement Learning
In this paper, we aim to tackle the task of semi-supervised video object segmentation across a sequence of frames where only the ground-truth segmentation of the first frame is provided. The challenges lie in how to onli…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Segmentation+4DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation
In this paper, we propose the differentiable mask-matching network (DMM-Net) for solving the video object segmentation problem where the initial object masks are provided. Relying on the Mask R-CNN backbone, we extract m…
ObjectOne-shot visual object segmentationRolling Shutter CorrectionSemantic Segmentation+3Joint-task Self-supervised Learning for Temporal Correspondence
This paper proposes to learn reliable dense correspondence from videos in a self-supervised manner. Our learning process integrates two highly related tasks: tracking large image regions \emph{and} establishing fine-grai…
Object TrackingSelf-Supervised LearningSemi-Supervised Video Object SegmentationUnsupervised Video Object SegmentationRANet: Ranking Attention Network for Fast Video Object Segmentation
Despite online learning (OL) techniques have boosted the performance of semi-supervised video object segmentation (VOS) methods, the huge time costs of OL greatly restrict their practicality. Matching based and propagati…
DecoderObjectSemantic SegmentationSemi-Supervised Video Object Segmentation+2An Efficient 3D CNN for Action/Object Segmentation in Video
Convolutional Neural Network (CNN) based image segmentation has made great progress in recent years. However, video object segmentation remains a challenging task due to its high computational complexity. Most of the pre…
Action SegmentationDecoderImage SegmentationObject+6A 3D Convolutional Approach to Spectral Object Segmentation in Space and Time
We formulate object segmentation in video as a graph partitioning problem in space and time, in which nodes are pixels and their relations form local neighborhoods. We claim that the strongest cluster in this pixel-level…
ClusteringGPUgraph partitioningInstance Segmentation+3Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation
Video object segmentation (VOS) aims at pixel-level object tracking given only the annotations in the first frame. Due to the large visual variations of objects in video and the lack of training samples, it remains a dif…
ObjectObject TrackingOne-shot visual object segmentationSegmentation+4Self-supervised Learning for Video Correspondence Flow
The objective of this paper is self-supervised learning of feature embeddings that are suitable for matching correspondences along the videos, which we term correspondence flow. By leveraging the natural spatial-temporal…
Self-Supervised LearningSemi-Supervised Video Object SegmentationUnsupervised Video Object SegmentationVideo Correspondence Flow+2