Papers Video Object Segmentation
“Video Object Segmentation” 태그가 달린 논문 608편 · 필터 해제
TarViS: A Unified Approach for Target-based Video Segmentation
The general domain of video segmentation is currently fragmented into different tasks spanning multiple benchmarks. Despite rapid progress in the state-of-the-art, current methods are overwhelmingly task-specific and can…
Instance SegmentationPanoptic SegmentationSegmentationSemantic Segmentation+5End-to-End Video Matting With Trimap Propagation
The research of video matting mainly focuses on temporal coherence and has gained significant improvement via neural networks. However, matting usually relies on user-annotated trimaps to estimate alpha values, which…
Image MattingSegmentationSemantic SegmentationVideo Matting+2Alignment Before Aggregation: Trajectory Memory Retrieval Network for Video Object Segmentation
Memory-based methods in semi-supervised video object segmentation task achieve competitive performance by performing dense matching between query and memory frames. However, most of the existing methods neglect the f…
RetrievalSemantic SegmentationSemi-Supervised Video Object SegmentationVideo Object Segmentation+1Segment Every Reference Object in Spatial and Temporal Spaces
The reference-based object segmentation tasks, namely referring image segmentation (RIS), referring video object segmentation (RVOS), and video object segmentation (VOS), aim to segment a specific object by utilizing…
Image SegmentationObjectReferring Expression SegmentationReferring Video Object Segmentation+4Unsupervised Video Object Segmentation with Online Adversarial Self-Tuning
The existing unsupervised video object segmentation methods depend heavily on the segmentation model trained offline on a labeled training video set, and cannot well generalize to the test videos from a different dom…
ObjectPseudo LabelSegmentationSemantic Segmentation+3Video State-Changing Object Segmentation
Daily objects commonly experience state changes. For example, slicing a cucumber changes its state from whole to sliced. Learning about object state changes in Video Object Segmentation (VOS) is crucial for understan…
ObjectRepresentation LearningSegmentationSemantic Segmentation+2HTML: Hybrid Temporal-scale Multimodal Learning Framework for Referring Video Object Segmentation
Referring Video Object Segmentation (RVOS) is to segment the object instance from a given video, according to the textual description of this object. However, in the open world, the object descriptions are often dive…
multimodal interactionObjectReferring Video Object SegmentationSemantic Segmentation+2Video Object Segmentation-aware Video Frame Interpolation
Video frame interpolation (VFI) is a very active research topic due to its broad applicability to many applications, including video enhancement, video encoding, and slow-motion effects. VFI methods have been advance…
ObjectPose EstimationSegmentationSemantic Segmentation+5SegGPT: Towards Segmenting Everything in Context
We present SegGPT, a generalist model for segmenting everything in context. We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data b…
Few-Shot Semantic SegmentationIn-Context LearningPanoptic SegmentationSegmentation+3Robust Referring Video Object Segmentation with Cyclic Structural Consensus
Referring Video Object Segmentation (R-VOS) is a challenging task that aims to segment an object in a video based on a linguistic expression. Most existing R-VOS methods have a critical assumption: the object referre…
ObjectReferring Video Object SegmentationSemantic SegmentationVideo Object Segmentation+11st Place Solution for YouTubeVOS Challenge 2022: Referring Video Object Segmentation
The task of referring video object segmentation aims to segment the object in the frames of a given video to which the referring expressions refer. Previous methods adopt multi-stage approach and design complex pipelines…
ObjectReferring Video Object SegmentationSemantic SegmentationVideo Object Segmentation+1Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy
We present IMAS, a method that segments the primary objects in videos without manual annotation in training or inference. Previous methods in unsupervised video object segmentation (UVOS) have demonstrated the effectiven…
MisconceptionsObjectObject DiscoverySegmentation+4Learning a Fast 3D Spectral Approach to Object Segmentation and Tracking over Space and Time
We pose video object segmentation as spectral graph clustering in space and time, with one graph node for each pixel and edges forming local space-time neighborhoods. We claim that the strongest cluster in this video gra…
ClusteringGPUGraph ClusteringObject+6Look Before You Match: Instance Understanding Matters in Video Object Segmentation
Exploring dense matching between the current frame and past frames for long-range context modeling, memory-based methods have demonstrated impressive results in video object segmentation (VOS) recently. Nevertheless, due…
Instance SegmentationSegmentationSemantic SegmentationSemi-Supervised Video Object Segmentation+2Breaking the "Object" in Video Object Segmentation
The appearance of an object can be fleeting when it transforms. As eggs are broken or paper is torn, their color, shape and texture can change dramatically, preserving virtually nothing of the original except for the ide…
ObjectSemantic SegmentationVideo Object SegmentationVideo Semantic SegmentationVideo Object of Interest Segmentation
In this work, we present a new computer vision task named video object of interest segmentation (VOIS). Given a video and a target image of interest, our objective is to simultaneously segment and track all objects in th…
DecoderObjectSegmentationSemantic Segmentation+2Learning to Learn Better for Video Object Segmentation
Recently, the joint learning framework (JOINT) integrates matching based transductive reasoning and online inductive learning to achieve accurate and robust semi-supervised video object segmentation (SVOS). However, usin…
Inductive LearningObjectSemantic SegmentationSemi-Supervised Video Object Segmentation+2Dual Prototype Attention for Unsupervised Video Object Segmentation
Unsupervised video object segmentation (VOS) aims to detect and segment the most salient object in videos. The primary techniques used in unsupervised VOS are 1) the collaboration of appearance and motion information; an…
ObjectSemantic SegmentationUnsupervised Video Object SegmentationVideo Object Segmentation+1LVOS: A Benchmark for Long-term Video Object Segmentation
Existing video object segmentation (VOS) benchmarks focus on short-term videos which just last about 3-5 seconds and where objects are visible most of the time. These videos are poorly representative of practical applica…
ObjectSemantic SegmentationVideo Object SegmentationVideo Semantic SegmentationVisual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview
Visual semantic segmentation aims at separating a visual sample into diverse blocks with specific semantic attributes and identifying the category for each block, and it plays a crucial role in environmental perception. …
SegmentationSemantic SegmentationVideo Object SegmentationVideo Semantic Segmentation+1