Papers with Code 아카이브
Video Semantic Segmentation
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The goal of video semantic segmentation is to assign a predefined class to each pixel in all frames of a video. This requires the model not only to predict accurate segmentation masks but also to ensure that these masks remain temporally consistent across frames. This task has broad applications in areas such as autonomous driving, medical video analysis, and AR/VR.
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
Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder
CogGen: A Learner-Centered Generative AI Architecture for Intelligent Tutoring with Programming Video
Leader360V: The Large-scale, Real-world 360 Video Dataset for Multi-task Learning in Diverse Environment
A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects
Q-SAM2: Accurate Quantization for Segment Anything Model 2
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
SAM-I2V: Upgrading SAM to Support Promptable Video Segmentation with Less than 0.2% Training Cost
OmniFall: A Unified Staged-to-Wild Benchmark for Human Fall Detection
Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System Collaboration
ThinkVideo: High-Quality Reasoning Video Segmentation with Chain of Thoughts
Unlocking the Power of SAM 2 for Few-Shot Segmentation
Long-RVOS: A Comprehensive Benchmark for Long-term Referring Video Object Segmentation
FlowCut: Unsupervised Video Instance Segmentation via Temporal Mask Matching
VolE: A Point-cloud Framework for Food 3D Reconstruction and Volume Estimation
6D Pose Estimation on Spoons and Hands
Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2
TEMPURA: Temporal Event Masked Prediction and Understanding for Reasoning in Action
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
MedSAM2: Segment Anything in 3D Medical Images and Videos
CamoSAM2: Motion-Appearance Induced Auto-Refining Prompts for Video Camouflaged Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.