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Semantic Segmentation

Papers

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Showing 9201–9225 of 14763 papers

TitleStatusHype
MixCL: Pixel label matters to contrastive learning—0
Mixed-Block Neural Architecture Search for Medical Image Segmentation—0
Mixed context networks for semantic segmentation—0
Mixed-domain Training Improves Multi-Mission Terrain Segmentation—0
Mixed Prototype Consistency Learning for Semi-supervised Medical Image Segmentation—0
Mixed-Query Transformer: A Unified Image Segmentation Architecture—0
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications to Parallel Machine Learning and Multi-Label Image Segmentation—0
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications—0
Mixed-Supervised Dual-Network for Medical Image Segmentation—0
Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference—0
Mixing Data Augmentation with Preserving Foreground Regions in Medical Image Segmentation—0
MixModule: Mixed CNN Kernel Module for Medical Image Segmentation—0
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model—0
MixReorg: Cross-Modal Mixed Patch Reorganization is a Good Mask Learner for Open-World Semantic Segmentation—0
Mixture Modeling of Global Shape Priors and Autoencoding Local Intensity Priors for Left Atrium Segmentation—0
Mixture-of-Shape-Experts (MoSE): End-to-End Shape Dictionary Framework to Prompt SAM for Generalizable Medical Segmentation—0
Mixtures of Neural Cellular Automata: A Stochastic Framework for Growth Modelling and Self-Organization—0
Mixup-CAM: Weakly-supervised Semantic Segmentation via Uncertainty Regularization—0
Mixup-Privacy: A simple yet effective approach for privacy-preserving segmentation—0
MKANet: A Lightweight Network with Sobel Boundary Loss for Efficient Land-cover Classification of Satellite Remote Sensing Imagery—0
MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation—0
MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation—0
MLAN: Multi-Level Adversarial Network for Domain Adaptive Semantic Segmentation—0
ML-BPM: Multi-teacher Learning with Bidirectional Photometric Mixing for Open Compound Domain Adaptation in Semantic Segmentation—0
MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning—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