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Semantic Segmentation

Papers

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Showing 6151–6175 of 14763 papers

TitleStatusHype
GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels—0
High Accuracy Pulmonary Vessel Segmentation for Contrast and Non-contrast CT Images and Clinical Evaluation—0
Construct to Associate: Cooperative Context Learning for Domain Adaptive Point Cloud Segmentation—0
High Accurate Unhealthy Leaf Detection—0
GCISG: Guided Causal Invariant Learning for Improved Syn-to-real Generalization—0
High-Definition Map Generation Technologies For Autonomous Driving—0
gcDLSeg: Integrating Graph-cut into Deep Learning for Binary Semantic Segmentation—0
Higher-Order Inference for Multi-Class Log-Supermodular Models—0
A Survey on Deep Learning Methods for Semantic Image Segmentation in Real-Time—0
A Closed-Loop System for Improving Annotation Quality and Efficiency—0
GBSS:a global building semantic segmentation dataset for large-scale remote sensing building extraction—0
High-Fidelity Image Inpainting with Multimodal Guided GAN Inversion—0
Constraints as Features—0
High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Vision—0
High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey—0
Intelligent image synthesis to attack a segmentation CNN using adversarial learning—0
Intensity and Texture Correction of Omnidirectional Image Using Camera Images for Indirect Augmented Reality—0
Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification—0
High-Order Paired-ASPP Networks for Semantic Segmenation—0
High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks—0
Gaze Estimation for Assisted Living Environments—0
Gazebo Plants: Simulating Plant-Robot Interaction with Cosserat Rods—0
Constraining Pseudo-label in Self-training Unsupervised Domain Adaptation with Energy-based Model—0
High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss—0
Gaze-Assisted Human-Centric Domain Adaptation for Cardiac Ultrasound 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