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Semantic Segmentation

Papers

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Showing 9176–9200 of 14763 papers

TitleStatusHype
MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images—0
Mind The Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation—0
Minimal Adversarial Examples for Deep Learning on 3D Point Clouds—0
Minimax Active Learning—0
Minimizing Energy Costs in Deep Learning Model Training: The Gaussian Sampling Approach—0
Minimizing Labeled, Maximizing Unlabeled: An Image-Driven Approach for Video Instance Segmentation—0
Minimum Class Confusion based Transfer for Land Cover Segmentation in Rural and Urban Regions—0
MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation—0
Mining of Single-Class by Active Learning for Semantic Segmentation—0
MIPR:Automatic Annotation of Medical Images with Pixel Rearrangement—0
MIRAM: Masked Image Reconstruction Across Multiple Scales for Breast Lesion Risk Prediction—0
MirrorNet: Bio-Inspired Camouflaged Object Segmentation—0
Mirror U-Net: Marrying Multimodal Fission with Multi-task Learning for Semantic Segmentation in Medical Imaging—0
Mirror-Yolo: A Novel Attention Focus, Instance Segmentation and Mirror Detection Model—0
Miss Detection vs. False Alarm: Adversarial Learning for Small Object Segmentation in Infrared Images—0
MISS: Memory-efficient Instance Segmentation Framework By Visual Inductive Priors Flow Propagation—0
MiSuRe is all you need to explain your image segmentation—0
Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals—0
Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data—0
Mitigating False Predictions In Unreasonable Body Regions—0
Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals—0
Mitigating the Impact of Attribute Editing on Face Recognition—0
Mitigating Undisciplined Over-Smoothing in Transformer for Weakly Supervised Semantic Segmentation—0
Mix and match networks: cross-modal alignment for zero-pair image-to-image translation—0
Mix-and-Match Tuning for Self-Supervised Semantic 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