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Semantic Segmentation

Papers

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Showing 6801–6825 of 14763 papers

TitleStatusHype
CondenseUNet: A Memory-Efficient Condensely-Connected Architecture for Bi-ventricular Blood Pool and Myocardium Segmentation—0
From Explanations to Segmentation: Using Explainable AI for Image Segmentation—0
From Deterministic to Probabilistic: A Novel Perspective on Domain Generalization for Medical Image Segmentation—0
Achieving Competitive Play Through Bottom-Up Approach in Semantic Segmentation—0
From colouring-in to pointillism: revisiting semantic segmentation supervision—0
Instance segmentation based 6D pose estimation of industrial objects using point clouds for robotic bin-picking—0
Instance Segmentation Based Graph Extraction for Handwritten Circuit Diagram Images—0
Instance Segmentation based Semantic Matting for Compositing Applications—0
Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image—0
From CNN to Transformer: A Review of Medical Image Segmentation Models—0
Assessing Systematic Weaknesses of DNNs using Counterfactuals—0
Learning Instance Activation Maps for Weakly Supervised Instance Segmentation—0
From CAD models to soft point cloud labels: An automatic annotation pipeline for cheaply supervised 3D semantic segmentation—0
From Bird's-Eye to Street View: Crafting Diverse and Condition-Aligned Images with Latent Diffusion Model—0
From a Fourier-Domain Perspective on Adversarial Examples to a Wiener Filter Defense for Semantic Segmentation—0
From Active Contours to Minimal Geodesic Paths: New Solutions to Active Contours Problems by Eikonal Equations—0
Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery—0
Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions—0
Concurrent Discrimination and Alignment for Self-Supervised Feature Learning—0
Learning Hyperspectral Feature Extraction and Classification with ResNeXt Network—0
FREST: Feature RESToration for Semantic Segmentation under Multiple Adverse Conditions—0
Concept Mask: Large-Scale Segmentation from Semantic Concepts—0
Learning Image Representations by Completing Damaged Jigsaw Puzzles—0
Learning Instance-Specific Adaptation for Cross-Domain Segmentation—0
FreqU-FNet: Frequency-Aware U-Net for Imbalanced Medical Image Segmentation—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