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Semantic Segmentation

Papers

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Showing 9726–9750 of 14763 papers

TitleStatusHype
On the calibration of underrepresented classes in LiDAR-based semantic segmentation—0
On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent—0
On the cost of homogeneous network building blocks and parameter sharing—0
On the dice loss gradient and the ways to mimic it—0
On the effectiveness of Rotation-Equivariance in U-Net: A Benchmark for Image Segmentation—0
On the Effect of Image Resolution on Semantic Segmentation—0
On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation—0
On the Effect of Pre-Processing and Model Complexity for Plastic Analysis Using Short-Wave-Infrared Hyper-Spectral Imaging—0
On the Efficacy of Multi-scale Data Samplers for Vision Applications—0
On the Feasibility and Generality of Patch-based Adversarial Attacks on Semantic Segmentation Problems—0
On-the-fly Point Feature Representation for Point Clouds Analysis—0
On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification—0
On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures—0
On the Importance of Label Quality for Semantic Segmentation—0
On the Importance of Visual Context for Data Augmentation in Scene Understanding—0
On the Influence of Shape, Texture and Color for Learning Semantic Segmentation—0
On the Matrix-Free Generation of Adversarial Perturbations for Black-Box Attacks—0
On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation—0
On the Pareto Efficiency of Quantized CNN—0
On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild—0
On the Robustness of Human Pose Estimation—0
On the robustness of self-supervised representations for multi-view object classification—0
On the status of current quantum machine learning software—0
On The Usage Of Average Hausdorff Distance For Segmentation Performance Assessment: Hidden Bias When Used For Ranking—0
OOD-SEG: Out-Of-Distribution detection for image SEGmentation with sparse multi-class positive-only annotations—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