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Semantic Segmentation

Papers

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Showing 7701–7725 of 14763 papers

TitleStatusHype
Leveraging Motion Priors in Videos for Improving Human Segmentation—0
Leveraging Multimodal-LLMs Assisted by Instance Segmentation for Intelligent Traffic Monitoring—0
Combining the Best of Graphical Models and ConvNets for Semantic Segmentation—0
Leveraging Open-Vocabulary Diffusion to Camouflaged Instance Segmentation—0
Detection of Customer Interested Garments in Surveillance Video using Computer Vision—0
Leveraging Perceptual Scores for Dataset Pruning in Computer Vision Tasks—0
A Semantic Knowledge Complementarity based Decoupling Framework for Semi-supervised Class-imbalanced Medical Image Segmentation—0
Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation—0
Leveraging Pretrained Image Classifiers for Language-Based Segmentation—0
Leveraging Road Area Semantic Segmentation with Auxiliary Steering Task—0
Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation—0
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation—0
MedSAM-CA: A CNN-Augmented ViT with Attention-Enhanced Multi-Scale Fusion for Medical Image Segmentation—0
Combining Supervised and Un-supervised Learning for Automatic Citrus Segmentation—0
3D Feature Distillation with Object-Centric Priors—0
Leveraging Superfluous Information in Contrastive Representation Learning—0
MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM—0
Leveraging Task-Specific Knowledge from LLM for Semi-Supervised 3D Medical Image Segmentation—0
MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation—0
MedSeg-R: Reasoning Segmentation in Medical Images with Multimodal Large Language Models—0
Memory-Augmented SAM2 for Training-Free Surgical Video Segmentation—0
Memory Efficient Transformer Adapter for Dense Predictions—0
Leveraging weak complementary labels to improve semantic segmentation of hepatocellular carcinoma and cholangiocarcinoma in H&E-stained slides—0
Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention—0
Few-shot pixel-precise document layout segmentation via dynamic instance generation and local thresholding—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