SOTAVerified

Semantic Segmentation

Papers

Showing 38763900 of 14763 papers

TitleStatusHype
Joint network for specular highlight detection and adversarial generation of specular-free images trained with polarimetric dataCode0
DEU-Net: Dual-Encoder U-Net for Automated Skin Lesion SegmentationCode1
ScribbleGen: Generative Data Augmentation Improves Scribble-supervised Semantic SegmentationCode0
TransNeXt: Robust Foveal Visual Perception for Vision TransformersCode2
Emergent Open-Vocabulary Semantic Segmentation from Off-the-shelf Vision-Language ModelsCode1
I-MedSAM: Implicit Medical Image Segmentation with Segment AnythingCode1
Parameter Efficient Fine-tuning via Cross Block Orchestration for Segment Anything Model0
Image segmentation with traveling waves in an exactly solvable recurrent neural network0
ContextSeg: Sketch Semantic Segmentation by Querying the Context with Attention0
Clean Label Disentangling for Medical Image Segmentation with Noisy LabelsCode0
SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language GuidanceCode1
Seeing Beyond Cancer: Multi-Institutional Validation of Object Localization and 3D Semantic Segmentation using Deep Learning for Breast MRI0
Segment Every Out-of-Distribution ObjectCode1
2D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentation0
RISAM: Referring Image Segmentation via Mutual-Aware Attention Features0
FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding0
Unleashing the Power of Prompt-driven Nucleus Instance SegmentationCode1
UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image RecognitionCode3
SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic SegmentationCode1
Where to Begin? From Random to Foundation Model Instructed Initialization in Federated Learning for Medical Image Segmentation0
Only Positive Cases: 5-fold High-order Attention Interaction Model for Skin Segmentation Derived ClassificationCode1
Unified Batch Normalization: Identifying and Alleviating the Feature Condensation in Batch Normalization and a Unified Framework0
Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person ImagesCode1
SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose EstimationCode2
Spatially Covariant Image Registration with Text PromptsCode1
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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