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Semantic Segmentation

Papers

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Showing 9901–9950 of 14763 papers

TitleStatusHype
Patch Correspondences for Interpreting Pixel-level CNNs—0
PatchCut: Data-Driven Object Segmentation via Local Shape Transfer—0
PatchFCN for Intracranial Hemorrhage Detection—0
PatchFormer: An Efficient Point Transformer with Patch Attention—0
Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning—0
Patch Network for medical image Segmentation—0
Patchwork: A Patch-wise Attention Network for Efficient Object Detection and Segmentation in Video Streams—0
Pathological Image Segmentation with Noisy Labels—0
Pathological myopia classification with simultaneous lesion segmentation using deep learning—0
Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation—0
Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentation—0
Pattern Integration and Enhancement Vision Transformer for Self-Supervised Learning in Remote Sensing—0
Pattern-Structure Diffusion for Multi-Task Learning—0
PAUMER: Patch Pausing Transformer for Semantic Segmentation—0
Pay Attention to the Atlas: Atlas-Guided Test-Time Adaptation Method for Robust 3D Medical Image Segmentation—0
PBP-Net: Point Projection and Back-Projection Network for 3D Point Cloud Segmentation—0
PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation—0
PCA-aided Fully Convolutional Networks for Semantic Segmentation of Multi-channel fMRI—0
PCA for Enhanced Cross-Dataset Generalizability in Breast Ultrasound Tumor Segmentation—0
PCAMs: Weakly Supervised Semantic Segmentation Using Point Supervision—0
PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training—0
PCCA-Model: an attention module for medical image segmentation—0
Fuzzy Positive Learning for Semi-supervised Semantic Segmentation—0
PCSCNet: Fast 3D Semantic Segmentation of LiDAR Point Cloud for Autonomous Car using Point Convolution and Sparse Convolution Network—0
PCT: Perspective Cue Training Framework for Multi-Camera BEV Segmentation—0
PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data—0
PDDM: Pseudo Depth Diffusion Model for RGB-PD Semantic Segmentation Based in Complex Indoor Scenes—0
PEA: Improving the Performance of ReLU Networks for Free by Using Progressive Ensemble Activations—0
PeakNet: An Autonomous Bragg Peak Finder with Deep Neural Networks—0
PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation—0
PECNet: A Deep Multi-Label Segmentation Network for Eosinophilic Esophagitis Biopsy Diagnostics—0
Peekaboo - Where are the Objects? Structure Adjusting Superpixels—0
PEg TRAnsfer Workflow recognition challenge report: Does multi-modal data improve recognition?—0
PeLK: Parameter-efficient Large Kernel ConvNets with Peripheral Convolution—0
Pelvic floor MRI segmentation based on semi-supervised deep learning—0
PE-MED: Prompt Enhancement for Interactive Medical Image Segmentation—0
PEMMA: Parameter-Efficient Multi-Modal Adaptation for Medical Image Segmentation—0
Penalizing small errors using an Adaptive Logarithmic Loss—0
Penalizing Top Performers: Conservative Loss for Semantic Segmentation Adaptation—0
Penalty-Based Imitation Learning With Cross Semantics Generation Sensor Fusion for Autonomous Driving—0
PennSyn2Real: Training Object Recognition Models without Human Labeling—0
PeP: a Point enhanced Painting method for unified point cloud tasks—0
Perceive, Excavate and Purify: A Novel Object Mining Framework for Instance Segmentation—0
Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey—0
Perception Framework through Real-Time Semantic Segmentation and Scene Recognition on a Wearable System for the Visually Impaired—0
Perception Matters: Enhancing Embodied AI with Uncertainty-Aware Semantic Segmentation—0
Perceptual Consistency in Video Segmentation—0
Perceptual Inductive Bias Is What You Need Before Contrastive Learning—0
Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images—0
Performance Analysis of Deep Learning Models for Femur Segmentation in MRI Scan—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
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