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Semantic Segmentation

Papers

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Showing 8976–9000 of 14763 papers

TitleStatusHype
SMAC-Seg: LiDAR Panoptic Segmentation via Sparse Multi-directional Attention Clustering—0
Small Objects Matters in Weakly-supervised Semantic Segmentation—0
Small Obstacle Avoidance Based on RGB-D Semantic Segmentation—0
Small Target Detection for Search and Rescue Operations using Distributed Deep Learning and Synthetic Data Generation—0
Small, Versatile and Mighty: A Range-View Perception Framework—0
SmartEraser: Remove Anything from Images using Masked-Region Guidance—0
Smart Hypothesis Generation for Efficient and Robust Room Layout Estimation—0
Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation—0
SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation—0
SML: Semantic Meta-learning for Few-shot Semantic Segmentation—0
Smooth Maximum Unit: Smooth Activation Function for Deep Networks Using Smoothing Maximum Technique—0
Snapshot Distillation: Teacher-Student Optimization in One Generation—0
SnapshotNet: Self-supervised Feature Learning for Point Cloud Data Segmentation Using Minimal Labeled Data—0
SNIDA: Unlocking Few-Shot Object Detection with Non-linear Semantic Decoupling Augmentation—0
Snow Mask Guided Adaptive Residual Network for Image Snow Removal—0
Snowy Night-to-Day Translator and Semantic Segmentation Label Similarity for Snow Hazard Indicator—0
SOccDPT: Semi-Supervised 3D Semantic Occupancy from Dense Prediction Transformers trained under memory constraints—0
SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving—0
SODA: Self-organizing data augmentation in deep neural networks -- Application to biomedical image segmentation tasks—0
Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions—0
Soft Masked Transformer for Point Cloud Processing with Skip Attention-Based Upsampling—0
Soft Neighbors are Positive Supporters in Contrastive Visual Representation Learning—0
SoftSeg: Advantages of soft versus binary training for image segmentation—0
Soft-Segmentation Guided Object Motion Deblurring—0
Software-Defined FPGA Accelerator Design for Mobile Deep Learning Applications—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InternImage-H (M3I Pre-training)Params (M)1,310—Unverified
2ViT-P (InternImage-H)Validation mIoU63.6—Unverified
3ONE-PEACEValidation mIoU63—Unverified
4M3I Pre-training (InternImage-H)Validation mIoU62.9—Unverified
5InternImage-HValidation mIoU62.9—Unverified
6BEiT-3Validation mIoU62.8—Unverified
7EVAValidation mIoU62.3—Unverified
8ViT-P (OneFormer, InternImage-H)Validation mIoU61.6—Unverified
9ViT-Adapter-L (Mask2Former, BEiTv2 pretrain)Validation mIoU61.5—Unverified
10FD-SwinV2-GValidation mIoU61.4—Unverified