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Semantic Segmentation

Papers

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Showing 9901–9950 of 14763 papers

TitleStatusHype
Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration—0
DFC: Deep Feature Consistency for Robust Point Cloud Registration—0
Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge—0
Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance SegmentationCode0
Background-Aware 3D Point Cloud Segmentationwith Dynamic Point Feature Aggregation—0
Deep Neural Networks for Automatic Grain-matrix Segmentation in Plane and Cross-polarized Sandstone Photomicrographs—0
Leveraging Unsupervised Image Registration for Discovery of Landmark Shape DescriptorCode0
Multiset Neurons—0
csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps—0
Clicking Matters:Towards Interactive Human Parsing—0
Evaluation of Deep Learning Topcoders Method for Neuron Individualization in Histological Macaque Brain Section—0
Explanatory Analysis and Rectification of the Pitfalls in COVID-19 Datasets—0
SUPER-Net: Trustworthy Medical Image Segmentation with Uncertainty Propagation in Encoder-Decoder Networks—0
Multi-Task Neural Processes—0
Unsupervised Spiking Instance Segmentation on Event Data using STDP—0
Real-time Instance Segmentation of Surgical Instruments using Attention and Multi-scale Feature Fusion—0
Robust deep learning-based semantic organ segmentation in hyperspectral images—0
Data privacy protection in microscopic image analysis for material data mining—0
Dual Prototypical Contrastive Learning for Few-shot Semantic SegmentationCode0
Multi-Modality Cardiac Image Analysis with Deep Learning—0
Segmentation of Multiple Myeloma Plasma Cells in Microscopy Images with Noisy Labels—0
Semantically Consistent Image-to-Image Translation for Unsupervised Domain Adaptation—0
SSA: Semantic Structure Aware Inference for Weakly Pixel-Wise Dense Predictions without Cost—0
DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder—0
FBNet: Feature Balance Network for Urban-Scene Segmentation—0
Attention on Classification for Fire Segmentation—0
LVIS Challenge Track Technical Report 1st Place Solution: Distribution Balanced and Boundary Refinement for Large Vocabulary Instance Segmentation—0
Towards Panoptic 3D Parsing for Single Image in the Wild—0
Temporal Fusion Based Mutli-scale Semantic Segmentation for Detecting Concealed Baggage Threats—0
Video Salient Object Detection via Contrastive Features and Attention Modules—0
Learned Image Compression for Machine Perception—0
HS3: Learning with Proper Task Complexity in Hierarchically Supervised Semantic Segmentation—0
3-D PET Image Generation with tumour masks using TGANCode0
CPSeg: Cluster-free Panoptic Segmentation of 3D LiDAR Point Clouds—0
Detect-and-Segment: a Deep Learning Approach to Automate Wound Image Segmentation—0
Explainable Medical Image Segmentation via Generative Adversarial Networks and Layer-wise Relevance PropagationCode0
A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation—0
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy—0
Geodesic Models with Convexity Shape Prior—0
Dense Prediction with Attentive Feature Aggregation—0
Pixel-Level Analysis for Enhancing Threat Detection in Large-Scale X-ray Security ImagesCode0
Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs—0
MedAI: Transparency in Medical Image SegmentationCode0
Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation—0
Creating A Coefficient of Change in the Built Environment After a Natural Disaster—0
Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation—0
Learning Debiased and Disentangled Representations for Semantic Segmentation—0
DRBANET: A Lightweight Dual-Resolution Network for Semantic Segmentation with Boundary Auxiliary—0
Calibrating the Dice loss to handle neural network overconfidence for biomedical image segmentationCode0
Focal Attention Networks: optimising attention for biomedical image segmentation—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