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Semantic Segmentation

Papers

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Showing 8226–8250 of 14763 papers

TitleStatusHype
LID 2020: The Learning from Imperfect Data Challenge Results—0
Lidar-based Norwegian tree species detection using deep learning—0
LiDAR-based Recurrent 3D Semantic Segmentation with Temporal Memory Alignment—0
LiDAR Based Semantic Perception for Forklifts in Outdoor Environments—0
LiDAR-Camera Fusion for Video Panoptic Segmentation without Video Training—0
LiDARFormer: A Unified Transformer-based Multi-task Network for LiDAR Perception—0
LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perception—0
LidarMultiNet: Unifying LiDAR Semantic Segmentation, 3D Object Detection, and Panoptic Segmentation in a Single Multi-task Network—0
LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation—0
Lifelong Learning Using a Dynamically Growing Tree of Sub-networks for Domain Generalization in Video Object Segmentation—0
LIF-Seg: LiDAR and Camera Image Fusion for 3D LiDAR Semantic Segmentation—0
Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D—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
LiftPool: Bidirectional ConvNet Pooling—0
Lift-the-flap: what, where and when for context reasoning—0
LightFormer: A lightweight and efficient decoder for remote sensing image segmentation—0
LIGHT: Joint Individual Building Extraction and Height Estimation from Satellite Images through a Unified Multitask Learning Network—0
Lightweight HDR Camera ISP for Robust Perception in Dynamic Illumination Conditions via Fourier Adversarial Networks—0
Lightweight Monocular Depth Estimation—0
Lightweight Road Environment Segmentation using Vector Quantization—0
Lightweight Uncertainty Quantification with Simplex Semantic Segmentation for Terrain Traversability—0
Lightweight Vision Transformer with Cross Feature Attention—0
Light-Weight Vision Transformer with Parallel Local and Global Self-Attention—0
LIME: Localized Image Editing via Attention Regularization in Diffusion Models—0
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All datasetsADE20KNYU-Depth V2Cityscapes testCityscapes valADE20K valPASCAL ContextS3DIS Area5PASCAL VOC 2012 testS3DISScanNetSUN-RGBDDensePASS

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