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Semantic Segmentation

Papers

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Showing 7226–7250 of 14763 papers

TitleStatusHype
Locally Adaptive Learning Loss for Semantic Image Segmentation—0
Complementary Bi-directional Feature Compression for Indoor 360° Semantic Segmentation with Self-distillation—0
Deep Neural Networks and PIDE discretizations—0
Deep Neural Networks for Automatic Grain-matrix Segmentation in Plane and Cross-polarized Sandstone Photomicrographs—0
LAECIPS: Large Vision Model Assisted Adaptive Edge-Cloud Collaboration for IoT-based Embodied Intelligence System—0
A Simple Approach for Visual Rearrangement: 3D Mapping and Semantic Search—0
Adversarial Transfer of Pose Estimation Regression—0
CompetitorFormer: Competitor Transformer for 3D Instance Segmentation—0
Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation—0
Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation—0
FLAVARS: A Multimodal Foundational Language and Vision Alignment Model for Remote Sensing—0
Adversarial training and dilated convolutions for brain MRI segmentation—0
Deep Neural Patchworks: Coping with Large Segmentation Tasks—0
FLARES: Fast and Accurate LiDAR Multi-Range Semantic Segmentation—0
Competitive Learning for Achieving Content-specific Filters in Video Coding for Machines—0
Flame quality monitoring of flare stack based on deep visual features—0
Competitive Ensembling Teacher-Student Framework for Semi-Supervised Left Atrium MRI Segmentation—0
Land Cover Semantic Segmentation Using ResUNet—0
ACE: Adapting to Changing Environments for Semantic Segmentation—0
Land use mapping in the Three Gorges Reservoir Area based on semantic segmentation deep learning method—0
Lane detection in complex scenes based on end-to-end neural network—0
Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks—0
FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation—0
ACDC: The Adverse Conditions Dataset with Correspondences for Robust Semantic Driving Scene Perception—0
A Simple and Generic Framework for Feature Distillation via Channel-wise Transformation—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