Papers Unsupervised Video Object Segmentation
“Unsupervised Video Object Segmentation” 태그가 달린 논문 93편 · 필터 해제
Reciprocal Transformations for Unsupervised Video Object Segmentation
Unsupervised video object segmentation (UVOS) aims at segmenting the primary objects in videos without any human intervention. Due to the lack of prior knowledge about the primary objects, identifying them from video…
ObjectOptical Flow EstimationSemantic SegmentationUnsupervised Video Object Segmentation+2Video Instance Segmentation with a Propose-Reduce Paradigm
Video instance segmentation (VIS) aims to segment and associate all instances of predefined classes for each frame in videos. Prior methods usually obtain segmentation for a frame or clip first, and merge the incomplete …
Instance SegmentationSegmentationSemantic SegmentationUnsupervised Video Object Segmentation+1Mask Selection and Propagation for Unsupervised Video Object Segmentation
In this work we present a novel approach for Unsupervised Video Object Segmentation, that is automatically generating instance level segmentation masks for salient objects and tracking them in a video. We efficiently han…
SegmentationSemantic SegmentationUnsupervised Video Object SegmentationVideo Object Segmentation+1Learning Motion-Appearance Co-Attention for Zero-Shot Video Object Segmentation
How to make the appearance and motion information interact effectively to accommodate complex scenarios is a fundamental issue in flow-based zero-shot video object segmentation. In this paper, we propose an Attentive…
Semantic SegmentationUnsupervised Video Object SegmentationVideo Object SegmentationVideo Semantic Segmentation+1Deep Transport Network for Unsupervised Video Object Segmentation
The popular unsupervised video object segmentation methods fuse the RGB frame and optical flow via a two-stream network. However, they cannot handle the distracting noises in each input modality, which may vastly det…
ObjectOptical Flow EstimationSemantic SegmentationUnsupervised Video Object Segmentation+2F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation
Although deep learning based methods have achieved great progress in unsupervised video object segmentation, difficult scenarios (e.g., visual similarity, occlusions, and appearance changing) are still not well-handled. …
Semantic SegmentationUnsupervised Video Object SegmentationVideo Object SegmentationVideo Semantic SegmentationMaking a Case for 3D Convolutions for Object Segmentation in Videos
The task of object segmentation in videos is usually accomplished by processing appearance and motion information separately using standard 2D convolutional networks, followed by a learned fusion of the two sources of in…
DecoderSegmentationSemantic SegmentationUnsupervised Video Object Segmentation+5MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object Segmentation
In this paper, we present a novel end-to-end learning neural network, i.e., MATNet, for zero-shot video object segmentation (ZVOS). Motivated by the human visual attention behavior, MATNet leverages motion cues as a bott…
ObjectSemantic SegmentationUnsupervised Video Object SegmentationVideo Object Segmentation+3DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping
We describe an unsupervised method to detect and segment portions of images of live scenes that, at some point in time, are seen moving as a coherent whole, which we refer to as objects. Our method first partitions the m…
Continual LearningObjectobject-detectionObject Detection+7Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation
In this paper, we introduce a novel network, called discriminative feature network (DFNet), to address the unsupervised video object segmentation task. To capture the inherent correlation among video frames, we learn dis…
RGB Salient Object DetectionSemantic SegmentationUnsupervised Video Object SegmentationVideo Object Segmentation+1Unsupervised Video Object Segmentation with Joint Hotspot Tracking
Object tracking is a well-studied problem in computer vision while identifying salient spots of objects in a video is a less explored direction in the literature. Video eye gaze estimation methods aim to tackle a related…
Gaze EstimationObjectObject TrackingSegmentation+4ALBA : Reinforcement Learning for Video Object Segmentation
We consider the challenging problem of zero-shot video object segmentation (VOS). That is, segmenting and tracking multiple moving objects within a video fully automatically, without any manual initialization. We treat t…
ObjectOne-shot visual object segmentationreinforcement-learningReinforcement Learning+6STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos
Existing methods for instance segmentation in videos typically involve multi-stage pipelines that follow the tracking-by-detection paradigm and model a video clip as a sequence of images. Multiple networks are used to de…
Instance SegmentationSemantic SegmentationUnsupervised Video Object SegmentationVideo Instance SegmentationLearning Video Object Segmentation from Unlabeled Videos
We propose a new method for video object segmentation (VOS) that addresses object pattern learning from unlabeled videos, unlike most existing methods which rely heavily on extensive annotated data. We introduce a unifie…
ObjectRepresentation LearningSegmentationSemantic Segmentation+5Motion-Attentive Transition for Zero-Shot Video Object Segmentation
In this paper, we present a novel Motion-Attentive Transition Network (MATNet) for zero-shot video object segmentation, which provides a new way of leveraging motion information to reinforce spatio-temporal object repres…
DecoderObjectSegmentationSemantic Segmentation+4MAST: 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+1Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks
This work proposes a novel attentive graph neural network (AGNN) for zero-shot video object segmentation (ZVOS). The suggested AGNN recasts this task as a process of iterative information fusion over video graphs. Specif…
Graph Neural NetworkSegmentationSemantic SegmentationUnsupervised Video Object Segmentation+4See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks
We introduce a novel network, called CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view. We emphasize the importance of inherent correlation among video…
Semantic SegmentationUnsupervised Video Object SegmentationVideo Object SegmentationVideo Polyp Segmentation+1UnOVOST: 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+3Anchor Diffusion for Unsupervised Video Object Segmentation
Unsupervised video object segmentation has often been tackled by methods based on recurrent neural networks and optical flow. Despite their complexity, these kinds of approaches tend to favour short-term temporal depende…
Image SegmentationObjectOptical Flow EstimationSemantic Segmentation+3