SOTAVerified

Semantic Segmentation

Papers

Showing 92769300 of 14763 papers

TitleStatusHype
Attention Toward Neighbors: A Context Aware Framework for High Resolution Image Segmentation0
VOLO: Vision Outlooker for Visual RecognitionCode1
Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image SegmentationCode1
Probabilistic Attention for Interactive SegmentationCode1
Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene ImagesCode2
FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object DetectionCode1
Exploiting Negative Learning for Implicit Pseudo Label Rectification in Source-Free Domain Adaptive Semantic Segmentation0
Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation0
Real-time Instance Segmentation with Discriminative Orientation MapsCode1
LayerCAM: Exploring Hierarchical Class Activation Maps for LocalizationCode1
Automatic Head Overcoat Thickness Measure with NASNet-Large-Decoder Net0
The Neurally-Guided Shape Parser: Grammar-based Labeling of 3D Shape Regions with Approximate InferenceCode0
P2T: Pyramid Pooling Transformer for Scene UnderstandingCode1
Tracking Instances as QueriesCode1
SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure0
VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning0
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental LearningCode1
MEAL: Manifold Embedding-based Active Learning0
A Curriculum-style Self-training Approach for Source-Free Semantic SegmentationCode0
EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic SegmentationCode1
Spatio-Temporal Multi-Task Learning Transformer for Joint Moving Object Detection and Segmentation0
Trinity: A No-Code AI platform for complex spatial datasets0
SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving0
Distilling effective supervision for robust medical image segmentation with noisy labels0
Fully automated quantification of in vivo viscoelasticity of prostate zones using magnetic resonance elastography with Dense U-net segmentation0
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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
4M3I Pre-training (InternImage-H)Validation mIoU62.9Unverified
5InternImage-HValidation 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