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Semantic Segmentation

Papers

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Showing 11601–11650 of 14763 papers

TitleStatusHype
Two-Phase Learning for Weakly Supervised Object Localization—0
Two-stage Joint Transductive and Inductive learning for Nuclei Segmentation—0
Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism—0
Two-Stage Multi-task Self-Supervised Learning for Medical Image Segmentation—0
Two stages for visual object tracking—0
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling—0
Two-Stream Networks for Object Segmentation in Videos—0
Two-Stream UNET Networks for Semantic Segmentation in Medical Images—0
U4D: Unsupervised 4D Dynamic Scene Understanding—0
UAV-CROWD: Violent and non-violent crowd activity simulator from the perspective of UAV—0
UAV LiDAR Point Cloud Segmentation of A Stack Interchange with Deep Neural Networks—0
RemoteNet: Remote Sensing Image Segmentation Network based on Global-Local Information—0
UAV (Unmanned Aerial Vehicles): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking—0
Ubernet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory—0
U-CE: Uncertainty-aware Cross-Entropy for Semantic Segmentation—0
UCP-Net: Unstructured Contour Points for Instance Segmentation—0
UCS: A Universal Model for Curvilinear Structure Segmentation—0
UDHF2-Net: Uncertainty-diffusion-model-based High-Frequency TransFormer Network for Remotely Sensed Imagery Interpretation—0
UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite—0
UGformer for Robust Left Atrium and Scar Segmentation Across Scanners—0
UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation—0
UI-Net: Interactive Artificial Neural Networks for Iterative Image Segmentation Based on a User Model—0
UltraCompression: Framework for High Density Compression of Ultrasound Volumes using Physics Modeling Deep Neural Networks—0
Ultrafast Focus Detection for Automated Microscopy—0
Ultra-High Resolution Segmentation via Boundary-Enhanced Patch-Merging Transformer—0
Ultrasound Image Segmentation of Thyroid Nodule via Latent Semantic Feature Co-Registration—0
ULTra: Unveiling Latent Token Interpretability in Transformer Based Understanding—0
UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving—0
Challenge Summary U-MedSAM: Uncertainty-aware MedSAM for Medical Image Segmentation—0
Unbalanced Feature Transport for Exemplar-based Image Translation—0
Unbiasing Semantic Segmentation For Robot Perception using Synthetic Data Feature Transfer—0
Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation—0
Uncertainty and Interpretability in Convolutional Neural Networks for Semantic Segmentation of Colorectal Polyps—0
Uncertainty Aware Active Learning for Reconfiguration of Pre-trained Deep Object-Detection Networks for New Target Domains—0
Uncertainty-Aware Adapter: Adapting Segment Anything Model (SAM) for Ambiguous Medical Image Segmentation—0
Bayesian Neural Networks for 2D MRI Segmentation—0
Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation—0
Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation—0
Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing—0
Uncertainty-aware Evidential Fusion-based Learning for Semi-supervised Medical Image Segmentation—0
Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation—0
Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Prediction—0
Uncertainty-Aware Multi-Parametric Magnetic Resonance Image Information Fusion for 3D Object Segmentation—0
Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation—0
Uncertainty-aware Perception Models for Off-road Autonomous Unmanned Ground Vehicles—0
Uncertainty Aware Proposal Segmentation for Unknown Object Detection—0
Uncertainty-Aware Pseudo Label Refinery for Domain Adaptive Semantic Segmentation—0
Uncertainty-aware self-training with expectation maximization basis transformation—0
Uncertainty-Aware Training of Neural Networks for Selective Medical Image Segmentation—0
Uncertainty-based method for improving poorly labeled segmentation datasets—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