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Semantic Segmentation

Papers

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Showing 7026–7050 of 14763 papers

TitleStatusHype
Forming A Random Field via Stochastic Cliques: From Random Graphs to Fully Connected Random Fields—0
Is Two-shot All You Need? A Label-efficient Approach for Video Segmentation in Breast Ultrasound—0
Deep Learning-Based 3D Instance and Semantic Segmentation: A Review—0
ISWSST: Index-space-wave State Superposition Transformers for Multispectral Remotely Sensed Imagery Semantic Segmentation—0
Deep Learning Based 3D Segmentation: A Survey—0
Comprehensive Multimodal Segmentation in Medical Imaging: Combining YOLOv8 with SAM and HQ-SAM Models—0
LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds—0
Iterative Deep Compression : Compressing Deep Networks for Classification and Semantic Segmentation—0
Iterative Deep Convolutional Encoder-Decoder Network for Medical Image Segmentation—0
Comprehensive Multi-Modal Interactions for Referring Image Segmentation—0
FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees—0
Deep Learning-based Bio-Medical Image Segmentation using UNet Architecture and Transfer Learning—0
Iterative Instance Segmentation—0
ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor Segmentation—0
A dynamic interactive learning framework for automated 3D medical image segmentation—0
Forest Inspection Dataset for Aerial Semantic Segmentation and Depth Estimation—0
ForestEyes Project: Conception, Enhancements, and Challenges—0
Forest and Water Bodies Segmentation Through Satellite Images Using U-Net—0
Open Compound Domain Adaptation—0
A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset—0
Less than Few: Self-Shot Video Instance Segmentation—0
Foreign object segmentation in chest x-rays through anatomy-guided shape insertion—0
Foreground object segmentation in RGB-D data implemented on GPU—0
Foreground Clustering for Joint Segmentation and Localization in Videos and Images—0
A dynamic graph-cuts method with integrated multiple feature maps for segmenting kidneys in ultrasound images—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