Papers Video Object Segmentation
“Video Object Segmentation” 태그가 달린 논문 608편 · 필터 해제
Semi-supervised Active Learning for Video Action Detection
In this work, we focus on label efficient learning for video action detection. We develop a novel semi-supervised active learning approach which utilizes both labeled as well as unlabeled data along with informative samp…
Action DetectionActive LearningPseudo LabelSemantic Segmentation+5Flexible visual prompts for in-context learning in computer vision
In this work, we address in-context learning (ICL) for the task of image segmentation, introducing a novel approach that adapts a modern Video Object Segmentation (VOS) technique for visual in-context learning. This adap…
Image SegmentationIn-Context LearningSegmentationSemantic Segmentation+2VIDiff: Translating Videos via Multi-Modal Instructions with Diffusion Models
Diffusion models have achieved significant success in image and video generation. This motivates a growing interest in video editing tasks, where videos are edited according to provided text descriptions. However, most e…
Semantic SegmentationVideo EditingVideo GenerationVideo Object Segmentation+1SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation
Unsupervised video object segmentation (UVOS) aims at detecting the primary objects in a given video sequence without any human interposing. Most existing methods rely on two-stream architectures that separately encode t…
Objectobject-detectionObject DetectionOptical Flow Estimation+6Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation
In this paper, we propose a simple yet effective approach for self-supervised video object segmentation (VOS). Our key insight is that the inherent structural dependencies present in DINO-pretrained Transformers can be l…
ClusteringObjectObject DiscoverySegmentation+4SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation
In-context segmentation aims at segmenting novel images using a few labeled example images, termed as "in-context examples", exploring content similarities between examples and the target. The resulting models can be gen…
Meta-LearningOne-Shot SegmentationSegmentationSemantic Segmentation+2Sketch-based Video Object Segmentation: Benchmark and Analysis
Reference-based video object segmentation is an emerging topic which aims to segment the corresponding target object in each video frame referred by a given reference, such as a language expression or a photo mask. Howev…
ObjectSegmentationSemantic SegmentationVideo Object Segmentation+1Learning the What and How of Annotation in Video Object Segmentation
Video Object Segmentation (VOS) is crucial for several applications, from video editing to video data generation. Training a VOS model requires an abundance of manually labeled training videos. The de-facto traditional w…
SegmentationSemantic SegmentationVideo EditingVideo Object Segmentation+1ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification
Most object-level mapping systems in use today make use of an upstream learned object instance segmentation model. If we want to teach them about a new object or segmentation class, we need to build a large dataset and r…
Instance SegmentationMulti-Object TrackingObjectobject-detection+6SpVOS: Efficient Video Object Segmentation with Triple Sparse Convolution
Semi-supervised video object segmentation (Semi-VOS), which requires only annotating the first frame of a video to segment future frames, has received increased attention recently. Among existing pipelines, the memory-ma…
ObjectSemantic SegmentationSemi-Supervised Video Object SegmentationVideo Object Segmentation+1Putting the Object Back into Video Object Segmentation
We present Cutie, a video object segmentation (VOS) network with object-level memory reading, which puts the object representation from memory back into the video object segmentation result. Recent works on VOS employ bo…
ObjectSegmentationSemantic SegmentationSemi-Supervised Video Object Segmentation+3Zero-Shot Open-Vocabulary Tracking with Large Pre-Trained Models
Object tracking is central to robot perception and scene understanding. Tracking-by-detection has long been a dominant paradigm for object tracking of specific object categories. Recently, large-scale pre-trained models …
ObjectObject TrackingOptical Flow EstimationScene Understanding+3Sub-token ViT Embedding via Stochastic Resonance Transformers
Vision Transformer (ViT) architectures represent images as collections of high-dimensional vectorized tokens, each corresponding to a rectangular non-overlapping patch. This representation trades spatial granularity for …
Depth EstimationDepth PredictionImage RetrievalObject Discovery+5CoralVOS: Dataset and Benchmark for Coral Video Segmentation
Coral reefs formulate the most valuable and productive marine ecosystems, providing habitat for many marine species. Coral reef surveying and analysis are currently confined to coral experts who invest substantial effort…
SegmentationSemantic SegmentationVideo Object SegmentationVideo Segmentation+1Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
Unsupervised video object segmentation (VOS) is a task that aims to detect the most salient object in a video without external guidance about the object. To leverage the property that salient objects usually have distinc…
ObjectOptical Flow EstimationSemantic SegmentationUnsupervised Video Object Segmentation+2Memory-Efficient Continual Learning Object Segmentation for Long Video
Recent state-of-the-art semi-supervised Video Object Segmentation (VOS) methods have shown significant improvements in target object segmentation accuracy when information from preceding frames is used in segmenting the …
Continual LearningObjectSegmentationSemantic Segmentation+3Adversarial Attacks on Video Object Segmentation with Hard Region Discovery
Video object segmentation has been applied to various computer vision tasks, such as video editing, autonomous driving, and human-robot interaction. However, the methods based on deep neural networks are vulnerable to ad…
Autonomous DrivingObjectSegmentationSemantic Segmentation+3PanoVOS: Bridging Non-panoramic and Panoramic Views with Transformer for Video Segmentation
Panoramic videos contain richer spatial information and have attracted tremendous amounts of attention due to their exceptional experience in some fields such as autonomous driving and virtual reality. However, existing …
Autonomous DrivingSegmentationSemantic SegmentationVideo Object Segmentation+2Fully Transformer-Equipped Architecture for End-to-End Referring Video Object Segmentation
Referring Video Object Segmentation (RVOS) requires segmenting the object in video referred by a natural language query. Existing methods mainly rely on sophisticated pipelines to tackle such cross-modal task, and do not…
ObjectReferring Video Object SegmentationSemantic SegmentationSemantic Similarity+3Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation
Unsupervised Video Object Segmentation (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge. However, previous methods do not fully use spatial-temporal context and f…
Semantic SegmentationUnsupervised Video Object SegmentationVideo Object SegmentationVideo Semantic Segmentation