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Video Panoptic Segmentation

Video Panoptic Segmentation is a computer vision task that extends panoptic segmentation by incorporating temporal dimension. That is, given a video sequence, the goal is to predict the semantic class of each pixel while consistently tracking object instances. Here, the pixels belonging to the same object instance should be assigned the same instance ID throughout the video sequence.

Papers

Showing 125 of 42 papers

TitleStatusHype
UniVS: Unified and Universal Video Segmentation with Prompts as QueriesCode3
Tracking Anything with Decoupled Video SegmentationCode3
MM-OR: A Large Multimodal Operating Room Dataset for Semantic Understanding of High-Intensity Surgical EnvironmentsCode2
Context-Aware Video Instance SegmentationCode2
PVO: Panoptic Visual OdometryCode2
Uni-DVPS: Unified Model for Depth-Aware Video Panoptic SegmentationCode1
DVIS++: Improved Decoupled Framework for Universal Video SegmentationCode1
A Simple Video Segmenter by Tracking Objects Along Axial TrajectoriesCode1
1st Place Solution for PVUW Challenge 2023: Video Panoptic SegmentationCode1
DVIS: Decoupled Video Instance Segmentation FrameworkCode1
Tube-Link: A Flexible Cross Tube Framework for Universal Video SegmentationCode1
TarViS: A Unified Approach for Target-based Video SegmentationCode1
Context-Aware Relative Object Queries To Unify Video Instance and Panoptic SegmentationCode1
Video K-Net: A Simple, Strong, and Unified Baseline for Video SegmentationCode1
Large-Scale Video Panoptic Segmentation in the Wild: A BenchmarkCode1
PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic SegmentationCode1
ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic SegmentationCode1
Video Panoptic SegmentationCode1
A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects0
LiDAR-Camera Fusion for Video Panoptic Segmentation without Video Training0
Balancing Shared and Task-Specific Representations: A Hybrid Approach to Depth-Aware Video Panoptic Segmentation0
MGNiceNet: Unified Monocular Geometric Scene UnderstandingCode0
Configurable Embodied Data Generation for Class-Agnostic RGB-D Video Segmentation0
1st Place Winner of the 2024 Pixel-level Video Understanding in the Wild (CVPR'24 PVUW) Challenge in Video Panoptic Segmentation and Best Long Video Consistency of Video Semantic Segmentation0
3rd Place Solution for PVUW Challenge 2024: Video Panoptic Segmentation0
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