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Semantic Segmentation

Papers

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Showing 4951–5000 of 14763 papers

TitleStatusHype
Interactive Image Segmentation with Cross-Modality Vision TransformersCode0
Line Graphics Digitization: A Step Towards Full AutomationCode0
Base Layer Efficiency in Scalable Human-Machine Coding—0
Exploiting Richness of Learned Compressed Representation of Images for Semantic Segmentation—0
Learning to Augment: Hallucinating Data for Domain Generalized Segmentation—0
EffSeg: Efficient Fine-Grained Instance Segmentation using Structure-Preserving SparsityCode0
Synchronous Image-Label Diffusion Probability Model with Application to Stroke Lesion Segmentation on Non-contrast CT—0
Semantic Segmentation on 3D Point Clouds with High Density Variations—0
Hierarchical Open-vocabulary Universal Image SegmentationCode2
Learning Degradation-Independent Representations for Camera ISP Pipelines—0
CGAM: Click-Guided Attention Module for Interactive Pathology Image Segmentation via Backpropagating Refinement—0
DifFSS: Diffusion Model for Few-Shot Semantic SegmentationCode1
RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object SegmentationCode1
MeT: A Graph Transformer for Semantic Segmentation of 3D Meshes—0
SAMAug: Point Prompt Augmentation for Segment Anything ModelCode1
Surgical fine-tuning for Grape Bunch Segmentation under Visual Domain ShiftsCode0
TomatoDIFF: On-plant Tomato Segmentation with Denoising Diffusion ModelsCode0
Pay Attention to the Atlas: Atlas-Guided Test-Time Adaptation Method for Robust 3D Medical Image Segmentation—0
Bidirectional Correlation-Driven Inter-Frame Interaction Transformer for Referring Video Object Segmentation—0
A region and category confidence-based multi-task network for carotid ultrasound image segmentation and classification—0
Intra- & Extra-Source Exemplar-Based Style Synthesis for Improved Domain GeneralizationCode1
Internal-External Boundary Attention Fusion for Glass Surface Segmentation—0
Efficient Subclass Segmentation in Medical ImagesCode0
All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning—0
Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene SegmentationCode0
MobileViG: Graph-Based Sparse Attention for Mobile Vision ApplicationsCode1
SyMFM6D: Symmetry-aware Multi-directional Fusion for Multi-View 6D Object Pose EstimationCode1
SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency—0
Obscured Wildfire Flame Detection By Temporal Analysis of Smoke Patterns Captured by Unmanned Aerial Systems—0
Prompting classes: Exploring the Power of Prompt Class Learning in Weakly Supervised Semantic SegmentationCode1
Multiscale Progressive Text Prompt Network for Medical Image Segmentation—0
Topological Data Analysis Guided Segment Anything Model Prompt Optimization for Zero-Shot Segmentation in Biological Imaging—0
Achieving RGB-D level Segmentation Performance from a Single ToF Camera—0
PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and ClassificationCode0
The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One ShotCode4
MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated DatasetCode2
MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation—0
Learning Nuclei Representations with Masked Image Modelling—0
M3Act: Learning from Synthetic Human Group Activities—0
Inter-Rater Uncertainty Quantification in Medical Image Segmentation via Rater-Specific Bayesian Neural NetworksCode0
Analysis of LiDAR Configurations on Off-road Semantic Segmentation Performance—0
SeMLaPS: Real-time Semantic Mapping with Latent Prior Networks and Quasi-Planar SegmentationCode1
RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation ModelCode2
Fast Marching Energy CNN—0
Let Segment Anything Help Image Dehaze—0
High-Quality Unknown Object Instance Segmentation via Quadruple Boundary Error RefinementCode1
Incremental Learning on Food Instance Segmentation—0
Chan-Vese Attention U-Net: An attention mechanism for robust segmentation—0
Land Cover Segmentation with Sparse Annotations from Sentinel-2 ImageryCode1
1M parameters are enough? A lightweight CNN-based model for medical image segmentationCode1
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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