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Semantic Segmentation

Papers

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Showing 10451–10500 of 14763 papers

TitleStatusHype
Residual-driven Fuzzy C-Means Clustering for Image Segmentation—0
Residual Graph Convolutional Network for Bird's-Eye-View Semantic Segmentation—0
Residual Moment Loss for Medical Image Segmentation—0
Residual-Sparse Fuzzy C-Means Clustering Incorporating Morphological Reconstruction and Wavelet frames—0
Residual Spatial Fusion Network for RGB-Thermal Semantic Segmentation—0
Reslicing Ultrasound Images for Data Augmentation and Vessel Reconstruction—0
RESMatch: Referring Expression Segmentation in a Semi-Supervised Manner—0
Resolution-Aware Design of Atrous Rates for Semantic Segmentation Networks—0
Resolving Inconsistent Semantics in Multi-Dataset Image Segmentation—0
Resolving Language and Vision Ambiguities Together: Joint Segmentation & Prepositional Attachment Resolution in Captioned Scenes—0
Resolving Language and Vision Ambiguities Together: Joint Segmentation \& Prepositional Attachment Resolution in Captioned Scenes—0
Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation—0
ResQ: Residual Quantization for Video Perception—0
RESSCAL3D: Resolution Scalable 3D Semantic Segmentation of Point Clouds—0
ReSTR: Convolution-free Referring Image Segmentation Using Transformers—0
Restricted Deformable Convolution based Road Scene Semantic Segmentation Using Surround View Cameras—0
Rethinking Alignment and Uniformity in Unsupervised Semantic Segmentation—0
Rethinking Class Activation Maps for Segmentation: Revealing Semantic Information in Shallow Layers by Reducing Noise—0
Rethinking Client Reweighting for Selfish Federated Learning—0
Rethinking Convolutional Semantic Segmentation Learning—0
Rethinking Cross-modal Interaction from a Top-down Perspective for Referring Video Object Segmentation—0
Rethinking domain generalization in medical image segmentation: One image as one domain—0
Rethinking Early-Fusion Strategies for Improved Multimodal Image Segmentation—0
Rethinking Exemplars for Continual Semantic Segmentation in Endoscopy Scenes: Entropy-based Mini-Batch Pseudo-Replay—0
Rethinking Few-Shot Medical Image Segmentation by SAM2: A Training-Free Framework with Augmentative Prompting and Dynamic Matching—0
Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation—0
Rethinking ImageNet Pre-training—0
Rethinking Image-to-Video Adaptation: An Object-centric Perspective—0
Rethinking Implicit Neural Representations for Vision Learners—0
Rethinking Information Loss in Medical Image Segmentation with Various-sized Targets—0
Rethinking Query-Key Pairwise Interactions in Vision Transformers—0
Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets—0
Rethinking Scanning Strategies with Vision Mamba in Semantic Segmentation of Remote Sensing Imagery: An Experimental Study—0
Rethinking Semantic Segmentation Evaluation for Explainability and Model Selection—0
Rethinking Task and Metrics of Instance Segmentation on 3D Point Clouds—0
Rethinking the editing of generative adversarial networks: a method to estimate editing vectors based on dimension reduction—0
Rethinking the Zigzag Flattening for Image Reading—0
Rethinking U-net Skip Connections for Biomedical Image Segmentation—0
Rethinking Video Segmentation with Masked Video Consistency: Did the Model Learn as Intended?—0
Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge—0
RethNet: Object-by-Object Learning for Detecting Facial Skin Problems—0
Retinal Image Segmentation with a Structure-Texture Demixing Network—0
Retinal Image Segmentation with Small Datasets—0
Retinal Microvasculature as Biomarker for Diabetes and Cardiovascular Diseases—0
Retinal Optic Disc Segmentation using Conditional Generative Adversarial Network—0
Retinal Vessel Segmentation under Extreme Low Annotation: A Generative Adversarial Network Approach—0
Retinal Vessel Segmentation with Deep Graph and Capsule Reasoning—0
Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks—0
Retrain or not retrain? -- efficient pruning methods of deep CNN networks—0
Retrieval and Localization with Observation Constraints—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