SOTAVerified

Video Semantic Segmentation

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.

Papers

Showing 1–10 of 895 papers

TitleStatusHype
SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction—0
Memory-Augmented SAM2 for Training-Free Surgical Video Segmentation—0
MUVOD: A Novel Multi-view Video Object Segmentation Dataset and A Benchmark for 3D Segmentation—0
Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and GrounderCode1
CogGen: A Learner-Centered Generative AI Architecture for Intelligent Tutoring with Programming Video—0
Leader360V: The Large-scale, Real-world 360 Video Dataset for Multi-task Learning in Diverse Environment—0
A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects—0
M^3-VOS: Multi-Phase, Multi-Transition, and Multi-Scenery Video Object SegmentationCode1
Q-SAM2: Accurate Quantization for Segment Anything Model 2—0
THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation—0
Show:102550
← PrevPage 1 of 90Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1WaSR-T (ResNet-101)Q60.1—Unverified
2TMANet (ResNet-50)Q57.5—Unverified
3CSANet (ResNet-101)Q49.1—Unverified