SOTAVerified

Semantic Segmentation

Papers

Showing 14261450 of 14763 papers

TitleStatusHype
CentripetalNet: Pursuing High-quality Keypoint Pairs for Object DetectionCode1
Adversarial Policy Gradient for Deep Learning Image AugmentationCode1
Cerberus Transformer: Joint Semantic, Affordance and Attribute ParsingCode1
3D Instances as 1D KernelsCode1
Learning Local and Global Temporal Contexts for Video Semantic SegmentationCode1
Affinity Space Adaptation for Semantic Segmentation Across DomainsCode1
Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian InferenceCode1
A Closer Look at Self-training for Zero-Label Semantic SegmentationCode1
COCO-Stuff: Thing and Stuff Classes in ContextCode1
AsymFormer: Asymmetrical Cross-Modal Representation Learning for Mobile Platform Real-Time RGB-D Semantic SegmentationCode1
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image SegmentationCode1
CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous DrivingCode1
A Semantic Change Detection Network Based on Boundary Detection and Task Interaction for High-Resolution Remote Sensing ImagesCode1
Dynamic Focus-aware Positional Queries for Semantic SegmentationCode1
Asymmetric Patch Sampling for Contrastive LearningCode1
A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice AttentionCode1
CompFeat: Comprehensive Feature Aggregation for Video Instance SegmentationCode1
CenterMask : Real-Time Anchor-Free Instance SegmentationCode1
Collaborative Video Object Segmentation by Multi-Scale Foreground-Background IntegrationCode1
End-to-End Egospheric Spatial MemoryCode1
CENet: Context Enhancement Network for Medical Image SegmentationCode1
COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image SegmentationCode1
A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed SupervisionCode1
A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic SegmentationCode1
Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic SegmentationCode1
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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