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Semantic Segmentation

Papers

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Showing 7376–7400 of 14763 papers

TitleStatusHype
Learning-Based Cost Functions for 3D and 4D Multi-Surface Multi-Object Segmentation of Knee MRI: Data from the Osteoarthritis Initiative—0
Learning-Based Keypoint Registration for Fetoscopic Mosaicking—0
Learning Better Features for Face Detection with Feature Fusion and Segmentation Supervision—0
Learning Better Registration to Learn Better Few-Shot Medical Image Segmentation: Authenticity, Diversity, and Robustness—0
Fine tuning U-Net for ultrasound image segmentation: which layers?—0
MACD R-CNN: An Abnormal Cell Nucleus Detection Method—0
Learning Cascaded Detection Tasks with Weakly-Supervised Domain Adaptation—0
Learning Category- and Instance-Aware Pixel Embedding for Fast Panoptic Segmentation—0
Learning Category-Specific 3D Shape Models From Weakly Labeled 2D Images—0
Fine-tuning vision foundation model for crack segmentation in civil infrastructures—0
Comparison of lidar semantic segmentation performance on the structured SemanticKITTI and off-road RELLIS-3D datasets—0
Fine-grained Synthesis of Unrestricted Adversarial Examples—0
Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networks—0
Learning Common Representation from RGB and Depth Images—0
Deformable Convolution Based Road Scene Semantic Segmentation of Fisheye Images in Autonomous Driving—0
Fine-Grained Semantic Segmentation of Motion Capture Data using Dilated Temporal Fully-Convolutional Networks—0
Comparison of Different Methods for Tissue Segmentation in Histopathological Whole-Slide Images—0
Deformable-Heatmap-Segmentation for Automobile Visual Perception—0
Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation—0
MacFormer: Semantic Segmentation with Fine Object Boundaries—0
Fine-grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images—0
Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning—0
Comparison of automatic prostate zones segmentation models in MRI images using U-net-like architectures—0
Comparison of atlas-based and neural-network-based semantic segmentation for DENSE MRI images—0
As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks?—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