Papers Semi-Supervised Video Object Segmentation
“Semi-Supervised Video Object Segmentation” 태그가 달린 논문 154편 · 필터 해제
MHP-VOS: Multiple Hypotheses Propagation for Video Object Segmentation
We address the problem of semi-supervised video object segmentation (VOS), where the masks of objects of interests are given in the first frame of an input video. To deal with challenging cases where objects are occluded…
Decision MakingObjectSemantic SegmentationSemi-Supervised Video Object Segmentation+2Spatiotemporal CNN for Video Object Segmentation
In this paper, we present a unified, end-to-end trainable spatiotemporal CNN model for VOS, which consists of two branches, i.e., the temporal coherence branch and the spatial segmentation branch. Specifically, the tempo…
ObjectSegmentationSemantic SegmentationSemi-Supervised Video Object Segmentation+4Video Object Segmentation using Space-Time Memory Networks
We propose a novel solution for semi-supervised video object segmentation. By the nature of the problem, available cues (e.g. video frame(s) with object masks) become richer with the intermediate predictions. However, th…
Interactive Video Object SegmentationObjectOne-shot visual object segmentationSemantic Segmentation+3Fast video object segmentation with Spatio-Temporal GANs
Learning descriptive spatio-temporal object models from data is paramount for the task of semi-supervised video object segmentation. Most existing approaches mainly rely on models that estimate the segmentation mask base…
DescriptiveObjectOne-shot visual object segmentationOptical Flow Estimation+4BubbleNets: Learning to Select the Guidance Frame in Video Object Segmentation by Deep Sorting Frames
Semi-supervised video object segmentation has made significant progress on real and challenging videos in recent years. The current paradigm for segmentation methods and benchmark datasets is to segment objects in video …
SegmentationSemantic SegmentationSemi-Supervised Video Object SegmentationVideo Object Segmentation+1Learning Correspondence from the Cycle-Consistency of Time
We introduce a self-supervised method for learning visual correspondence from unlabeled video. The main idea is to use cycle-consistency in time as free supervisory signal for learning visual representations from scratch…
Optical Flow EstimationSemantic SegmentationSemi-Supervised Video Object SegmentationUnsupervised Video Object Segmentation+2RVOS: End-to-End Recurrent Network for Video Object Segmentation
Multiple object video object segmentation is a challenging task, specially for the zero-shot case, when no object mask is given at the initial frame and the model has to find the objects to be segmented along the sequenc…
GPUObjectOne-shot visual object segmentationSegmentation+4FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation
Many of the recent successful methods for video object segmentation (VOS) are overly complicated, heavily rely on fine-tuning on the first frame, and/or are slow, and are hence of limited practical use. In this work, we …
ObjectSegmentationSemantic SegmentationSemi-Supervised Video Object Segmentation+2Fast Online Object Tracking and Segmentation: A Unifying Approach
In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach. Our method, dubbed SiamMask, improves the offline trainin…
ObjectObject TrackingReal-Time Visual TrackingSegmentation+6A Generative Appearance Model for End-to-end Video Object Segmentation
One of the fundamental challenges in video object segmentation is to find an effective representation of the target and background appearance. The best performing approaches resort to extensive fine-tuning of a convoluti…
GPUOne-shot visual object segmentationSegmentationSemantic Segmentation+3VideoMatch: Matching based Video Object Segmentation
Video object segmentation is challenging yet important in a wide variety of applications for video analysis. Recent works formulate video object segmentation as a prediction task using deep nets to achieve appealing stat…
MemorizationObjectSegmentationSemantic Segmentation+3YouTube-VOS: Sequence-to-Sequence Video Object Segmentation
Learning long-term spatial-temporal features are critical for many video analysis tasks. However, existing video segmentation methods predominantly rely on static image segmentation techniques, and methods capturing temp…
Image SegmentationObjectOne-shot visual object segmentationOptical Flow Estimation+7PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation
We address semi-supervised video object segmentation, the task of automatically generating accurate and consistent pixel masks for objects in a video sequence, given the first-frame ground truth annotations. Towards this…
ObjectOne-shot visual object segmentationSegmentationSemantic Segmentation+3ReConvNet: Video Object Segmentation with Spatio-Temporal Features Modulation
We introduce ReConvNet, a recurrent convolutional architecture for semi-supervised video object segmentation that is able to fast adapt its features to focus on any specific object of interest at inference time. Generali…
ObjectPositionSemantic SegmentationSemi-Supervised Video Object Segmentation+2Fast and Accurate Online Video Object Segmentation via Tracking Parts
基于视频的目标检测算法研究
Semantic SegmentationSemi-Supervised Video Object SegmentationVideo Object SegmentationVideo Semantic Segmentation+1Fast Video Object Segmentation by Reference-Guided Mask Propagation
We present an efficient method for the semi-supervised video object segmentation. Our method achieves accuracy competitive with state-of-the-art methods while running in a fraction of time compared to others. To this end…
DecoderObjectobject-detectionObject Detection+6Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning
This paper tackles the problem of video object segmentation, given some user annotation which indicates the object of interest. The problem is formulated as pixel-wise retrieval in a learned embedding space: we embed pix…
Metric LearningObjectRetrievalSegmentation+6CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRF
This paper addresses the problem of video object segmentation, where the initial object mask is given in the first frame of an input video. We propose a novel spatio-temporal Markov Random Field (MRF) model defined over …
ObjectOne-Shot SegmentationOptical Flow EstimationSegmentation+4Video Object Segmentation with Language Referring Expressions
Most state-of-the-art semi-supervised video object segmentation methods rely on a pixel-accurate mask of a target object provided for the first frame of a video. However, obtaining a detailed segmentation mask is expensi…
ObjectReferring Expression SegmentationSegmentationSemantic Segmentation+3Efficient Video Object Segmentation via Network Modulation
Video object segmentation targets at segmenting a specific object throughout a video sequence, given only an annotated first frame. Recent deep learning based approaches find it effective by fine-tuning a general-purpose…
ObjectSegmentationSemantic SegmentationSemi-Supervised Video Object Segmentation+4