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Semi-Supervised Video Object Segmentation
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The semi-supervised scenario assumes the user inputs a full mask of the object(s) of interest in the first frame of a video sequence. Methods have to produce the segmentation mask for that object(s) in the subsequent frames.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Exploring Enhanced Contextual Information for Video-Level Object Tracking
A Distractor-Aware Memory for Visual Object Tracking with SAM2
LiVOS: Light Video Object Segmentation with Gated Linear Matching
Memory Matching is not Enough: Jointly Improving Memory Matching and Decoding for Video Object Segmentation
SAM 2: Segment Anything in Images and Videos
Global Motion Understanding in Large-Scale Video Object Segmentation
Spatial-Temporal Multi-level Association for Video Object Segmentation
Efficient Video Object Segmentation via Modulated Cross-Attention Memory
Video Object Segmentation with Dynamic Query Modulation
Augmenting Efficient Real-time Surgical Instrument Segmentation in Video with Point Tracking and Segment Anything
Lester: rotoscope animation through video object segmentation and tracking
ODTrack: Online Dense Temporal Token Learning for Visual Tracking
SpVOS: Efficient Video Object Segmentation with Triple Sparse Convolution
Putting the Object Back into Video Object Segmentation
Sub-token ViT Embedding via Stochastic Resonance Transformers
Memory-Efficient Continual Learning Object Segmentation for Long Video
Tracking Anything with Decoupled Video Segmentation
XMem++: Production-level Video Segmentation From Few Annotated Frames
Tracking Anything in High Quality
Hierarchical Spatiotemporal Transformers for Video Object Segmentation
ZJU ReLER Submission for EPIC-KITCHEN Challenge 2023: Semi-Supervised Video Object Segmentation
TrickVOS: A Bag of Tricks for Video Object Segmentation
READMem: Robust Embedding Association for a Diverse Memory in Unconstrained Video Object Segmentation
Video Object Segmentation in Panoptic Wild Scenes
Robust and Efficient Memory Network for Video Object Segmentation
CLVOS23: A Long Video Object Segmentation Dataset for Continual Learning
MobileVOS: Real-Time Video Object Segmentation Contrastive Learning meets Knowledge Distillation
Flow-guided Semi-supervised Video Object Segmentation
TarViS: A Unified Approach for Target-based Video Segmentation
Look Before You Match: Instance Understanding Matters in Video Object Segmentation
Learning to Learn Better for Video Object Segmentation
Decoupling Features in Hierarchical Propagation for Video Object Segmentation
Global Spectral Filter Memory Network for Video Object Segmentation
Pixel-Level Equalized Matching for Video Object Segmentation
SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-Maximization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.