Papers with Code 아카이브
Video Object Segmentation
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Video object segmentation is a binary labeling problem aiming to separate foreground object(s) from the background region of a video.
For leaderboards please refer to the different subtasks.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Memory-Augmented SAM2 for Training-Free Surgical Video Segmentation
MUVOD: A Novel Multi-view Video Object Segmentation Dataset and A Benchmark for 3D Segmentation
THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation
VideoMolmo: Spatio-Temporal Grounding Meets Pointing
InterRVOS: Interaction-aware Referring Video Object Segmentation
Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System Collaboration
ThinkVideo: High-Quality Reasoning Video Segmentation with Chain of Thoughts
Long-RVOS: A Comprehensive Benchmark for Long-term Referring Video Object Segmentation
Video-GPT via Next Clip Diffusion
6D Pose Estimation on Spoons and Hands
Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2
MoSAM: Motion-Guided Segment Anything Model with Spatial-Temporal Memory Selection
RGB-D Video Object Segmentation via Enhanced Multi-store Feature Memory
Few-Shot Referring Video Single- and Multi-Object Segmentation via Cross-Modal Affinity with Instance Sequence Matching
DC-SAM: In-Context Segment Anything in Images and Videos via Dual Consistency
PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild
MASSeg : 2nd Technical Report for 4th PVUW MOSE Track
FVOS for MOSE Track of 4th PVUW Challenge: 3rd Place Solution
Multi-person Physics-based Pose Estimation for Combat Sports
STSeg-Complex Video Object Segmentation: The 1st Solution for 4th PVUW MOSE Challenge
GLUS: Global-Local Reasoning Unified into A Single Large Language Model for Video Segmentation
Saliency-Motion Guided Trunk-Collateral Network for Unsupervised Video Object Segmentation
The 1st Solution for 4th PVUW MeViS Challenge: Unleashing the Potential of Large Multimodal Models for Referring Video Segmentation
CamoSAM2: Motion-Appearance Induced Auto-Refining Prompts for Video Camouflaged Object Detection
4th PVUW MeViS 3rd Place Report: Sa2VA
Zero-Shot 4D Lidar Panoptic Segmentation
ReferDINO-Plus: 2nd Solution for 4th PVUW MeViS Challenge at CVPR 2025
Reducing Annotation Burden: Exploiting Image Knowledge for Few-Shot Medical Video Object Segmentation via Spatiotemporal Consistency Relearning
One-Shot Medical Video Object Segmentation via Temporal Contrastive Memory Networks
AUTV: Creating Underwater Video Datasets with Pixel-wise Annotations
Leveraging Motion Information for Better Self-Supervised Video Correspondence Learning
Investigation of Frame Differences as Motion Cues for Video Object Segmentation
Find First, Track Next: Decoupling Identification and Propagation in Referring Video Object Segmentation
An Analysis of Data Transformation Effects on Segment Anything 2
Wandering around: A bioinspired approach to visual attention through object motion sensitivity
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