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Semantic Segmentation

Papers

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Showing 6626–6650 of 14763 papers

TitleStatusHype
ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models—0
Fully Convolutional Deep Network Architectures for Automatic Short Glass Fiber Semantic Segmentation from CT scans—0
Improving Multi-Modal Learning with Uni-Modal Teachers—0
Fully Convolutional Adaptation Networks for Semantic Segmentation—0
Improving Normalization with the James-Stein Estimator—0
Improving Online Performance Prediction for Semantic Segmentation—0
Improving Open-Set Semantic Segmentation in 3D Point Clouds by Conditional Channel Capacity Maximization: Preliminary Results—0
Improving Pallet Detection Using Synthetic Data—0
Improving Performance of Semantic Segmentation CycleGANs by Noise Injection into the Latent Segmentation Space—0
Improving Pixel-based MIM by Reducing Wasted Modeling Capability—0
Improving Pixel Embedding Learning through Intermediate Distance Regression Supervision for Instance Segmentation—0
Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection—0
Improving Polyphonic Sound Event Detection on Multichannel Recordings with the Sørensen-Dice Coefficient Loss and Transfer Learning—0
Fully Connected Deep Structured Networks—0
Improving Predictive Performance and Calibration by Weight Fusion in Semantic Segmentation—0
Fully Complex-valued Fully Convolutional Multi-feature Fusion Network (FC2MFN) for Building Segmentation of InSAR images—0
Improving RANSAC-Based Segmentation Through CNN Encapsulation—0
3rd Place Scheme on Instance Segmentation Track of ICCV 2021 VIPriors Challenges—0
Improving Robustness of Semantic Segmentation to Motion-Blur Using Class-Centric Augmentation—0
Improving Segment Anything on the Fly: Auxiliary Online Learning and Adaptive Fusion for Medical Image Segmentation—0
Associating Inter-Image Salient Instances for Weakly Supervised Semantic Segmentation—0
Fully-Automatic Synapse Prediction and Validation on a Large Data Set—0
Improving Semantic Segmentation in Transformers using Hierarchical Inter-Level Attention—0
Improving Semantic Segmentation of Aerial Images Using Patch-based Attention—0
When Segmentation is Not Enough: Rectifying Visual-Volume Discordance Through Multisensor Depth-Refined Semantic Segmentation for Food Intake Tracking in Long-Term Care—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