Papers Video Object Segmentation
“Video Object Segmentation” 태그가 달린 논문 608편 · 필터 해제
Learning 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+1Video Object Segmentation With Dynamic Memory Networks and Adaptive Object Alignment
In this paper, we propose a novel solution for object-matching based semi-supervised video object segmentation, where the target object masks in the first frame are provided. Existing object-matching based methods fo…
ObjectSemantic SegmentationSemi-Supervised Video Object SegmentationVideo Object 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+2Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation
Current state-of-the-art approaches for Semi-supervised Video Object Segmentation (Semi-VOS) propagates information from previous frames to generate segmentation mask for the current frame. This results in high-quality s…
One-shot visual object segmentationSegmentationSemantic SegmentationSemi-Supervised Video Object Segmentation+2Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation
This paper addresses the task of segmenting class-agnostic objects in semi-supervised setting. Although previous detection based methods achieve relatively good performance, these approaches extract the best proposal by …
Graph Neural NetworkObjectOne-shot visual object segmentationSemantic Segmentation+3Contrastive Transformation for Self-supervised Correspondence Learning
In this paper, we focus on the self-supervised learning of visual correspondence using unlabeled videos in the wild. Our method simultaneously considers intra- and inter-video representation associations for reliable cor…
Object TrackingSelf-Supervised LearningSemantic SegmentationVideo 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 SegmentationMake One-Shot Video Object Segmentation Efficient Again
Video object segmentation (VOS) describes the task of segmenting a set of objects in each frame of a video. In the semi-supervised setting, the first mask of each object is provided at test time. Following the one-shot p…
Objectobject-detectionObject DetectionOne-shot visual object segmentation+5TTVOS: Lightweight Video Object Segmentation with Adaptive Template Attention Module and Temporal Consistency Loss
Semi-supervised video object segmentation (semi-VOS) is widely used in many applications. This task is tracking class-agnostic objects from a given target mask. For doing this, various approaches have been developed base…
ObjectObject LocalizationOptical Flow EstimationSemantic Segmentation+4Reducing the Annotation Effort for Video Object Segmentation Datasets
For further progress in video object segmentation (VOS), larger, more diverse, and more challenging datasets will be necessary. However, densely labeling every frame with pixel masks does not scale to large datasets. We …
ObjectSemantic SegmentationVideo Object SegmentationVideo Semantic SegmentationDelving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation
In this paper, we address several inadequacies of current video object segmentation pipelines. Firstly, a cyclic mechanism is incorporated to the standard semi-supervised process to produce more robust representations. B…
ObjectOne-shot visual object segmentationSegmentationSemantic Segmentation+3Learning to Sort Image Sequences via Accumulated Temporal Differences
Consider a set of n images of a scene with dynamic objects captured with a static or a handheld camera. Let the temporal order in which these images are captured be unknown. There can be n! possibilities for the temporal…
Action RecognitionSemantic SegmentationVideo Object SegmentationVideo Semantic SegmentationVideo Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement
We propose a new matching-based framework for semi-supervised video object segmentation (VOS). Recently, state-of-the-art VOS performance has been achieved by matching-based algorithms, in which feature banks are created…
SegmentationSemantic SegmentationSemi-Supervised Video Object SegmentationVideo Object Segmentation+1Collaborative Video Object Segmentation by Multi-Scale Foreground-Background Integration
This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation. Unlike previous practices that focus on exploring the embedding learning of foreground obj…
ObjectOne-shot visual object segmentationSegmentationSemantic Segmentation+3Hybrid-S2S: Video Object Segmentation with Recurrent Networks and Correspondence Matching
One-shot Video Object Segmentation~(VOS) is the task of pixel-wise tracking an object of interest within a video sequence, where the segmentation mask of the first frame is given at inference time. In recent years, Recur…
One-shot visual object segmentationSegmentationSemantic SegmentationVideo Object Segmentation+1RefVOS: A Closer Look at Referring Expressions for Video Object Segmentation
The task of video object segmentation with referring expressions (language-guided VOS) is to, given a linguistic phrase and a video, generate binary masks for the object to which the phrase refers. Our work argues that e…
Image SegmentationReferring Expression SegmentationSegmentationVideo Object SegmentationLearning Spatio-Appearance Memory Network for High-Performance Visual Tracking
Existing visual object tracking usually learns a bounding-box based template to match the targets across frames, which cannot accurately learn a pixel-wise representation, thereby being limited in handling severe appeara…
Object TrackingSegmentationSemantic SegmentationVideo Object Segmentation+4PMVOS: Pixel-Level Matching-Based Video Object Segmentation
Semi-supervised video object segmentation (VOS) aims to segment arbitrary target objects in video when the ground truth segmentation mask of the initial frame is provided. Due to this limitation of using prior knowledge …
ObjectOne-shot visual object segmentationSegmentationSemantic Segmentation+3Ground-truth or DAER: Selective Re-query of Secondary Information
Many vision tasks use secondary information at inference time -- a seed -- to assist a computer vision model in solving a problem. For example, an initial bounding box is needed to initialize visual object tracking. To d…
Object TrackingScene ClassificationSemantic SegmentationVideo Object Segmentation+3LSMVOS: Long-Short-Term Similarity Matching for Video Object
Objective Semi-supervised video object segmentation refers to segmenting the object in subsequent frames given the object label in the first frame. Existing algorithms are mostly based on the objectives of matching and p…
ObjectOptical Flow EstimationSegmentationSemantic Segmentation+3