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Segmentation

Papers

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Showing 10451–10475 of 13072 papers

TitleStatusHype
Incorporating Domain Knowledge To Improve Topic Segmentation Of Long MOOC Lecture Videos—0
Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation—0
Incorporating Inner-word and Out-word Features for Mongolian Morphological Segmentation—0
Number-Adaptive Prototype Learning for 3D Point Cloud 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
Numerical Uncertainty of Convolutional Neural Networks Inference for Structural Brain MRI Analysis—0
Incorporating Uncertain Segmentation Information into Chinese NER for Social Media Text—0
NUMSnet: Nested-U Multi-class Segmentation network for 3D Medical Image Stacks—0
An End-to-end 3D Convolutional Neural Network for Action Detection and Segmentation in Videos—0
Incremental Class Discovery for Semantic Segmentation with RGBD Sensing—0
An Empirical Study on the Robustness of the Segment Anything Model (SAM)—0
Incremental Few-Shot Semantic Segmentation via Embedding Adaptive-Update and Hyper-class Representation—0
Incremental Learning for Fully Unsupervised Word Segmentation Using Penalized Likelihood and Model Selection—0
Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI—0
Incremental Learning for Multi-organ Segmentation with Partially Labeled Datasets—0
An Empirical Study on the Fairness of Foundation Models for Multi-Organ Image Segmentation—0
Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation—0
Incremental Learning of Affix Segmentation—0
Incremental Learning on Food Instance Segmentation—0
An Empirical Study on Multi-Domain Robust Semantic Segmentation—0
Incrementer: Transformer for Class-Incremental Semantic Segmentation With Knowledge Distillation Focusing on Old Class—0
An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models—0
Towards Deployable OCR models for Indic languages—0
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All datasetsSA-1B.MFSDMMFloodSimGas

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1unSAM+ (Semi-supervised)Average Precision42.8—Unverified
2SAMAverage Precision38.9—Unverified
#ModelMetricClaimedVerifiedStatus
1HNN10%20—Unverified
#ModelMetricClaimedVerifiedStatus
1ABANetF1 Score96.82—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50 + DeepLabV3+F1 score0.77—Unverified
#ModelMetricClaimedVerifiedStatus
1LangGasIoU0.69—Unverified