Papers Unsupervised Video Object Segmentation
“Unsupervised Video Object Segmentation” 태그가 달린 논문 93편 · 필터 해제
EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency
The existing approaches for salient motion segmentation are unable to explicitly learn geometric cues and often give false detections on prominent static objects. We exploit multiview geometric constraints to avoid such …
Motion SegmentationOptical Flow EstimationUnsupervised Video Object SegmentationVideo Object SegmentationJoint-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 SegmentationSemi-Supervised Video Salient Object Detection Using Pseudo-Labels
Deep learning-based video salient object detection has recently achieved great success with its performance significantly outperforming any other unsupervised methods. However, existing data-driven approaches heavily rel…
object-detectionRGB Salient Object DetectionSalient Object DetectionUnsupervised Video Object Segmentation+1Global Optimality Guarantees for Nonconvex Unsupervised Video Segmentation
In this paper, we consider the problem of unsupervised video object segmentation via background subtraction. Specifically, we pose the nonsemantic extraction of a video's moving objects as a nonconvex optimization proble…
ObjectSegmentationSemantic SegmentationUnsupervised Video Object Segmentation+3A 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+3Key Instance Selection for Unsupervised Video Object Segmentation
This paper proposes key instance selection based on video saliency covering objectness and dynamics for unsupervised video object segmentation (UVOS). Our method takes frames sequentially and extracts object proposals wi…
ObjectSegmentationSemantic SegmentationUnsupervised Video Object Segmentation+2Learning Unsupervised Video Object Segmentation Through Visual Attention
This paper conducts a systematic study on the role of visual attention in Unsupervised Video Object Segmentation (UVOS) tasks. By elaborately annotating three popular video segmentation datasets (DAVIS, Youtube-Objects a…
ObjectSegmentationSemantic SegmentationUnsupervised Video Object Segmentation+3Self-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+2Learning 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+4Unsupervised Moving Object Detection via Contextual Information Separation
We propose an adversarial contextual model for detecting moving objects in images. A deep neural network is trained to predict the optical flow in a region using information from everywhere else but that region (context)…
Moving Object DetectionObjectobject-detectionObject Detection+2Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation
Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects. It becomes tremendously challenging when multiple objects occur and interact in a…
Instance SegmentationObjectSegmentationSemantic Segmentation+3Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation
One major technique debt in video object segmentation is to label the object masks for training instances. As a result, we propose to prepare inexpensive, yet high quality pseudo ground truth corrected with motion cue fo…
Instance SegmentationObjectObject TrackingSegmentation+4Tukey-Inspired Video Object Segmentation
We investigate the problem of strictly unsupervised video object segmentation, i.e., the separation of a primary object from background in video without a user-provided object mask or any training on an annotated dataset…
ObjectSegmentationSemantic SegmentationUnsupervised Video Object Segmentation+2Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting
Video object segmentation is an essential task in robot manipulation to facilitate grasping and learning affordances. Incremental learning is important for robotics in unstructured environments, since the total number of…
Incremental LearningRobot ManipulationSegmentationSemantic Segmentation+3Unsupervised Online Video Object Segmentation with Motion Property Understanding
Unsupervised video object segmentation aims to automatically segment moving objects over an unconstrained video without any user annotation. So far, only few unsupervised online methods have been reported in literature a…
Motion DetectionObjectSegmentationSemantic Segmentation+3Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation
Unsupervised video segmentation plays an important role in a wide variety of applications from object identification to compression. However, to date, fast motion, motion blur and occlusions pose significant challenges. …
Deep LearningOptical Flow EstimationSaliency PredictionSegmentation+6Unsupervised Video Object Segmentation with Motion-based Bilateral Networks
In this work, we study the unsupervised video object segmentation problem where moving objects are segmented without prior knowledge of these objects. First, we propose a motion-based bilateral network to estimate the ba…
ObjectSegmentationSemantic SegmentationUnsupervised Video Object Segmentation+3Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection
This paper proposes a fast video salient object detection model, based on a novel recurrent network architecture, named Pyramid Dilated Bidirectional ConvLSTM (PDB-ConvLSTM). A Pyramid Dilated Convolution (PDC) module is…
GPUObjectobject-detectionObject Detection+7Extending Layered Models to 3D Motion
We consider the problem of inferring a layered representa-tion, its depth ordering and motion segmentation from a video in whichobjects may undergo 3D non-planar motion relative to the camera. Wegeneralize layered infere…
Motion SegmentationObjectSegmentationUnsupervised Video Object Segmentation