SOTAVerified

Semantic Segmentation

Papers

Showing 25012525 of 14763 papers

TitleStatusHype
DeformUX-Net: Exploring a 3D Foundation Backbone for Medical Image Segmentation with Depthwise Deformable ConvolutionCode1
DeiSAM: Segment Anything with Deictic PromptingCode1
DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point CloudsCode1
Implicit-Zoo: A Large-Scale Dataset of Neural Implicit Functions for 2D Images and 3D ScenesCode1
Co-segmentation Inspired Attention Module for Video-based Computer Vision TasksCode1
Annotator Consensus Prediction for Medical Image Segmentation with Diffusion ModelsCode1
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR SegmentationCode1
A Closer Look at the Explainability of Contrastive Language-Image Pre-trainingCode1
Delving Deeper into Anti-aliasing in ConvNetsCode1
CLIP-VIS: Adapting CLIP for Open-Vocabulary Video Instance SegmentationCode1
CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic SegmentationCode1
AADG: Automatic Augmentation for Domain Generalization on Retinal Image SegmentationCode1
Co-Scale Conv-Attentional Image TransformersCode1
On the Connection between Local Attention and Dynamic Depth-wise ConvolutionCode1
HSPFormer: Hierarchical Spatial Perception Transformer for Semantic SegmentationCode1
DenoiseRep: Denoising Model for Representation LearningCode1
CORE: Cooperative Reconstruction for Multi-Agent PerceptionCode1
COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-trainingCode1
Advancing Semantic Future Prediction through Multimodal Visual Sequence TransformersCode1
Extract Free Dense Labels from CLIPCode1
Dense Contrastive Learning for Self-Supervised Visual Pre-TrainingCode1
Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot SegmentationCode1
A Re-Parameterized Vision Transformer (ReVT) for Domain-Generalized Semantic SegmentationCode1
DenseHybrid: Hybrid Anomaly Detection for Dense Open-set RecognitionCode1
A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic InformationCode1
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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