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Semantic Segmentation

Papers

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Showing 7726–7750 of 14763 papers

TitleStatusHype
Combining the Best of Graphical Models and ConvNets for Semantic Segmentation—0
A Semantic Knowledge Complementarity based Decoupling Framework for Semi-supervised Class-imbalanced Medical Image Segmentation—0
Memory Matching is not Enough: Jointly Improving Memory Matching and Decoding for Video Object Segmentation—0
Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation—0
LID 2020: The Learning from Imperfect Data Challenge Results—0
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation—0
Memory Efficient Transformer Adapter for Dense Predictions—0
Lidar-based Norwegian tree species detection using deep learning—0
LiDAR-based Recurrent 3D Semantic Segmentation with Temporal Memory Alignment—0
Combining Supervised and Un-supervised Learning for Automatic Citrus Segmentation—0
3D Feature Distillation with Object-Centric Priors—0
Memory-guided Network with Uncertainty-based Feature Augmentation for Few-shot Semantic Segmentation—0
MeNToS: Tracklets Association with a Space-Time Memory Network—0
Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention—0
LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perception—0
Few-shot pixel-precise document layout segmentation via dynamic instance generation and local thresholding—0
LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation—0
Combining Self-attention and Dilation Convolutional for Semantic Segmentation of Coal Maceral Groups—0
Combining Pyramid Pooling and Attention Mechanism for Pelvic MR Image Semantic Segmentaion—0
A Semantic Consistency Feature Alignment Object Detection Model Based on Mixed-Class Distribution Metrics—0
LIF-Seg: LiDAR and Camera Image Fusion for 3D LiDAR Semantic Segmentation—0
Combining Multi-level Contexts of Superpixel using Convolutional Neural Networks to perform Natural Scene Labeling—0
Lifting by Gaussians: A Simple, Fast and Flexible Method for 3D Instance Segmentation—0
Lifting GIS Maps into Strong Geometric Context for Scene Understanding—0
Few-Shot Meta-Learning on Point Cloud for Semantic Segmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InternImage-H (M3I Pre-training)Params (M)1,310—Unverified
2ViT-P (InternImage-H)Validation mIoU63.6—Unverified
3ONE-PEACEValidation mIoU63—Unverified
4M3I Pre-training (InternImage-H)Validation mIoU62.9—Unverified
5InternImage-HValidation mIoU62.9—Unverified
6BEiT-3Validation mIoU62.8—Unverified
7EVAValidation mIoU62.3—Unverified
8ViT-P (OneFormer, InternImage-H)Validation mIoU61.6—Unverified
9ViT-Adapter-L (Mask2Former, BEiTv2 pretrain)Validation mIoU61.5—Unverified
10FD-SwinV2-GValidation mIoU61.4—Unverified