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Semantic Segmentation

Papers

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Showing 10301–10325 of 14763 papers

TitleStatusHype
Improving Semi-Supervised Semantic Segmentation with Sliced-Wasserstein Feature Alignment and Uniformity—0
Improving Spatial Codification in Semantic Segmentation—0
Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context—0
Improving task-specific representation via 1M unlabelled images without any extra knowledge—0
Improving the accuracy of neural networks in analog computing-in-memory systems by a generalized quantization method—0
Improving the Explain-Any-Concept by Introducing Nonlinearity to the Trainable Surrogate Model—0
Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder—0
Improving Training on Noisy Stuctured Labels—0
Improving Translation Invariance in Convolutional Neural Networks with Peripheral Prediction Padding—0
Improving Uncertainty-based Out-of-Distribution Detection for Medical Image Segmentation—0
Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy—0
Improving Unsupervised Video Object Segmentation via Fake Flow Generation—0
Improving Weakly-Supervised Object Localization By Micro-Annotation—0
Incomplete Multimodal Learning for Remote Sensing Data Fusion—0
In-context learning for medical image segmentation—0
In-Context Translation: Towards Unifying Image Recognition, Processing, and Generation—0
Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation—0
Incorporating Depth into both CNN and CRF for Indoor Semantic Segmentation—0
Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation—0
Incorporating Human Domain Knowledge in 3D LiDAR-based Semantic Segmentation—0
Incorporating Near-Infrared Information into Semantic Image Segmentation—0
Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation—0
Incorporating prior knowledge in medical image segmentation: a survey—0
Increasing the Robustness of Semantic Segmentation Models with Painting-by-Numbers—0
Incremental Class Discovery for Semantic Segmentation with RGBD Sensing—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