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Semantic Segmentation

Papers

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Showing 5351–5400 of 14763 papers

TitleStatusHype
Segmentation of the veterinary cytological images for fast neoplastic tumors diagnosis—0
Living in a Material World: Learning Material Properties from Full-Waveform Flash Lidar Data for Semantic Segmentation—0
AdaptiveClick: Clicks-aware Transformer with Adaptive Focal Loss for Interactive Image SegmentationCode1
Prompt What You Need: Enhancing Segmentation in Rainy Scenes with Anchor-based Prompting—0
Structural and Statistical Texture Knowledge Distillation for Semantic Segmentation—0
White Matter Hyperintensities Segmentation Using Probabilistic TransUNet—0
Semantic Segmentation using Vision Transformers: A survey—0
MAF-Net: Multiple attention-guided fusion network for fundus vascular image segmentation—0
Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural NetworkCode0
Clothes Grasping and Unfolding Based on RGB-D Semantic Segmentation—0
BadSAM: Exploring Security Vulnerabilities of SAM via Backdoor Attacks—0
Towards Segment Anything Model (SAM) for Medical Image Segmentation: A SurveyCode0
HAISTA-NET: Human Assisted Instance Segmentation Through Attention—0
Conditional and Residual Methods in Scalable Coding for Humans and MachinesCode0
Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with UncertaintyCode1
Smaller3d: Smaller Models for 3D Semantic Segmentation Using Minkowski Engine and Knowledge Distillation MethodsCode0
UPDExplainer: an Interpretable Transformer-based Framework for Urban Physical Disorder Detection Using Street View Imagery—0
Personalize Segment Anything Model with One ShotCode3
MTLSegFormer: Multi-task Learning with Transformers for Semantic Segmentation in Precision Agriculture—0
OctFormer: Octree-based Transformers for 3D Point CloudsCode2
Mitigating Undisciplined Over-Smoothing in Transformer for Weakly Supervised Semantic Segmentation—0
Point2Tree(P2T) -- framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest—0
UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentation—0
SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything ModelCode2
Mci-net: multi-scale context integrated network for liver ct image segmentationCode0
Revisiting the Encoding of Satellite Image Time SeriesCode1
Semi-Supervised Segmentation of Functional Tissue Units at the Cellular LevelCode1
Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous drivingCode1
CLUSTSEG: Clustering for Universal SegmentationCode1
Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN—0
Extraction of volumetric indices from echocardiography: which deep learning solution for clinical use?Code1
LineFormer: Rethinking Line Chart Data Extraction as Instance SegmentationCode1
Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth LabelsCode1
DeepAqua: Self-Supervised Semantic Segmentation of Wetland Surface Water Extent with SAR Images using Knowledge DistillationCode1
An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation ProblemsCode1
Exploring vision transformer layer choosing for semantic segmentation—0
Neural LiDAR Fields for Novel View Synthesis—0
RT-K-Net: Revisiting K-Net for Real-Time Panoptic SegmentationCode1
Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation—0
MASNet: A Robust Deep Marine Animal Segmentation NetworkCode0
MARS: Mask Attention Refinement with Sequential Quadtree Nodes for Car Damage Instance SegmentationCode1
CLIP-S^4: Language-Guided Self-Supervised Semantic Segmentation—0
Bidirectional Copy-Paste for Semi-Supervised Medical Image SegmentationCode2
Point Cloud Semantic Segmentation—0
PRSeg: A Lightweight Patch Rotate MLP Decoder for Semantic Segmentation—0
Rethinking Boundary Detection in Deep Learning Models for Medical Image SegmentationCode1
TaskPrompter: Spatial-Channel Multi-Task Prompting for Dense Scene UnderstandingCode2
Detecting Novelties with Empty Classes—0
Learning Structured Output Representations from Attributes using Deep Conditional Generative Models—0
Brain Tumor Segmentation from MRI Images using Deep Learning Techniques—0
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All datasetsADE20KNYU-Depth V2Cityscapes testCityscapes valADE20K valPASCAL ContextS3DIS Area5PASCAL VOC 2012 testS3DISScanNetSUN-RGBDDensePASS

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
4InternImage-HValidation mIoU62.9—Unverified
5M3I Pre-training (InternImage-H)Validation 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