SOTAVerified

Semantic Segmentation

Papers

Showing 876900 of 14763 papers

TitleStatusHype
ALSO: Automotive Lidar Self-supervision by Occupancy estimationCode1
Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label FusionCode1
Alternate Diverse Teaching for Semi-supervised Medical Image SegmentationCode1
Active Token MixerCode1
1st Place Solution for the 5th LSVOS Challenge: Video Instance SegmentationCode1
Active Negative Loss: A Robust Framework for Learning with Noisy LabelsCode1
DenseMTL: Cross-task Attention Mechanism for Dense Multi-task LearningCode1
Active learning for medical image segmentation with stochastic batchesCode1
Cross-view Transformers for real-time Map-view Semantic SegmentationCode1
CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological ImagesCode1
CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image SegmentationCode1
Cross-Domain Few-Shot Semantic SegmentationCode1
CSFNet: A Cosine Similarity Fusion Network for Real-Time RGB-X Semantic Segmentation of Driving ScenesCode1
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
CSWin-UNet: Transformer UNet with Cross-Shaped Windows for Medical Image SegmentationCode1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
Curriculum-style Local-to-global Adaptation for Cross-domain Remote Sensing Image SegmentationCode1
Cross-attention Spatio-temporal Context Transformer for Semantic Segmentation of Historical MapsCode1
Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentationCode1
Cross-Domain Few-Shot Semantic Segmentation via Doubly Matching TransformationCode1
Cross-Layer Retrospective Retrieving via Layer AttentionCode1
1st Place Solution for the UVO Challenge on Image-based Open-World Segmentation 2021Code1
Amodal Cityscapes: A New Dataset, its Generation, and an Amodal Semantic Segmentation Challenge BaselineCode1
Amodal Ground Truth and Completion in the WildCode1
Cross-Patch Dense Contrastive Learning for Semi-Supervised Segmentation of Cellular Nuclei in Histopathologic ImagesCode1
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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