SOTAVerified

Semantic Segmentation

Papers

Showing 25762600 of 14763 papers

TitleStatusHype
Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image SegmentationCode1
Image Compositing for Segmentation of Surgical Tools without Manual AnnotationsCode1
CP2: Copy-Paste Contrastive Pretraining for Semantic SegmentationCode1
A Robust Feature Downsampling Module for Remote Sensing Visual TasksCode1
Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack DetectorsCode1
Image Manipulation Detection by Multi-View Multi-Scale SupervisionCode1
DINOv2 based Self Supervised Learning For Few Shot Medical Image SegmentationCode1
Adversarial Dual-Student with Differentiable Spatial Warping for Semi-Supervised Semantic SegmentationCode1
DIOD: Self-Distillation Meets Object DiscoveryCode1
Directional Connectivity-based Segmentation of Medical ImagesCode1
I-MedSAM: Implicit Medical Image Segmentation with Segment AnythingCode1
CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image SegmentationCode1
Illumination-based Transformations Improve Skin Lesion Segmentation in Dermoscopic ImagesCode1
Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image SegmentationCode1
COCO-Stuff: Thing and Stuff Classes in ContextCode1
Discriminative Region Suppression for Weakly-Supervised Semantic SegmentationCode1
Disentangle then Parse:Night-time Semantic Segmentation with Illumination DisentanglementCode1
CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt TuningCode1
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic SegmentationCode1
Learning Spatio-Appearance Memory Network for High-Performance Visual TrackingCode1
Disentangled Representations for Domain-generalized Cardiac SegmentationCode1
Disentangled Non-Local Neural NetworksCode1
Lester: rotoscope animation through video object segmentation and trackingCode1
ACCT is a fast and accessible automatic cell counting tool using machine learning for 2D image segmentationCode1
Illumination Controllable Dehazing Network based on Unsupervised Retinex EmbeddingCode1
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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