SOTAVerified

Semantic Segmentation

Papers

Showing 66266650 of 14763 papers

TitleStatusHype
Efficient few-shot learning for pixel-precise handwritten document layout analysis0
Open-vocabulary Semantic Segmentation with Frozen Vision-Language ModelsCode1
UNet-2022: Exploring Dynamics in Non-isomorphic Architecture0
Fast and Efficient Scene Categorization for Autonomous Driving using VAEs0
IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image SegmentationCode0
A Stronger Baseline For Automatic Pfirrmann Grading Of Lumbar Spine MRI Using Deep Learning0
Analyzing Deep Learning Representations of Point Clouds for Real-Time In-Vehicle LiDAR Perception0
How precise are performance estimates for typical medical image segmentation tasks?0
Boosting Semi-Supervised Semantic Segmentation with Probabilistic RepresentationsCode1
Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency GuidanceCode0
RGB-T Semantic Segmentation with Location, Activation, and SharpeningCode1
SemFormer: Semantic Guided Activation Transformer for Weakly Supervised Semantic SegmentationCode1
MEW-UNet: Multi-axis representation learning in frequency domain for medical image segmentationCode1
From colouring-in to pointillism: revisiting semantic segmentation supervision0
Instance Segmentation for Chinese Character Stroke Extraction, Datasets and BenchmarksCode1
Learning Explicit Object-Centric Representations with Vision Transformers0
ConnectedUNets++: Mass Segmentation from Whole Mammographic Images0
MISm: A Medical Image Segmentation Metric for Evaluation of weak labeled DataCode1
Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping0
Towards an efficient Iris Recognition System on Embedded Devices0
BARS: A Benchmark for Airport Runway SegmentationCode1
Large Batch and Patch Size Training for Medical Image Segmentation0
Brain Tumor Segmentation using Enhanced U-Net Model with Empirical AnalysisCode0
Towards Comprehensive Representation Enhancement in Semantics-guided Self-supervised Monocular Depth Estimation0
Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InternImage-H (M3I Pre-training)Params (M)1,310Unverified
2ViT-P (InternImage-H)Validation mIoU63.6Unverified
3ONE-PEACEValidation mIoU63Unverified
4InternImage-HValidation mIoU62.9Unverified
5M3I Pre-training (InternImage-H)Validation mIoU62.9Unverified
6BEiT-3Validation mIoU62.8Unverified
7EVAValidation mIoU62.3Unverified
8ViT-P (OneFormer, InternImage-H)Validation mIoU61.6Unverified
9ViT-Adapter-L (Mask2Former, BEiTv2 pretrain)Validation mIoU61.5Unverified
10FD-SwinV2-GValidation mIoU61.4Unverified