Segmentation Accuracy

Segmentation accuracy, the precision of delineating objects or regions within images, is a crucial aspect of many fields, particularly medical image analysis and remote sensing. Current research focuses on improving accuracy through advanced model architectures like U-Net and its variants, Transformers, and novel loss functions designed to address challenges such as class imbalance and small object detection. These advancements are driving improvements in diagnostic accuracy, treatment planning, and automated analysis across diverse applications, impacting both scientific understanding and practical outcomes.

Papers

June 4, 2022

PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers
Jiacong Xu, Zixiang Xiong, Shankar P. Bhattacharyya
Convolutional Neural Network Segmentation Accuracy W Net Branch Network Boundary Attention Proportional Integral Derivative

May 20, 2022

HCFormer: Unified Image Segmentation with Hierarchical Clustering
Teppei Suzuki
Semantic Segmentation Instance Segmentation Image Segmentation Segmentation Mask Segmentation Accuracy Hierarchical Clustering

April 25, 2022

PointInst3D: Segmenting 3D Instances by Points
Tong He, Wei Yin, Chunhua Shen, Anton van den Hengel
Exceptional Point Segmentation Accuracy 3D Instance Segmentation 3D Point Point Cloud Instance Segmentation 3D Instance

April 14, 2022

Unsupervised Deep Learning Meets Chan-Vese Model
Dihan Zheng, Chenglong Bao, Zuoqiang Shi, Haibin Ling, Kaisheng Ma
Image Segmentation Segmentation Accuracy Unsupervised Segmentation Unsupervised Deep Learning Unsupervised Image Segmentation

March 10, 2022

Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentation
Sukesh Adiga, Jose Dolz, Herve Lombaert
Segmentation Network Segmentation Accuracy Semi Supervised Segmentation Pixel Wise Uncertainty Label Representation

March 8, 2022

February 27, 2022

Topology-Preserving Segmentation Network: A Deep Learning Segmentation Framework for Connected Component
Han Zhang, Lok Ming Lui
Learning Based Segmentation Network Segmentation Accuracy Connected Component Topology Preserving

February 1, 2022

Mars Terrain Segmentation with Less Labels
Edwin Goh, Jingdao Chen, Brian Wilson
Segmentation Accuracy Terrain Segmentation

January 19, 2022

Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree Species Classification Based on Explanation Methods
Steve Ahlswede, Nimisha Thekke-Madam, Christian Schulz, Birgit Kleinschmit, Begüm Demir
Semantic Segmentation Remote Sensing Image Weakly Supervised Semantic Segmentation Tree Specie Segmentation Accuracy Explanation Method Image Level Label Class Activation

January 17, 2022

Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning
Yiwen Li, Yunguan Fu, Qianye Yang, Zhe Min, Wen Yan, Henkjan Huisman, Dean Barratt, Victor Adrian Prisacariu, Yipeng Hu
Medical Image Segmentation Medical Image Shot Segmentation Segmentation Accuracy Prototypical Learning Volumetric Medical Shot 3D

January 4, 2022

Towards Unsupervised Open World Semantic Segmentation
Svenja Uhlemeyer, Matthias Rottmann, Hanno Gottschalk
Semantic Segmentation Incremental Learning Segmentation Accuracy Open World Semantic Segmentation Segmentation Quality Assessment

November 18, 2021

Interactive segmentation using U-Net with weight map and dynamic user interactions
Ragavie Pirabaharan, Naimul Khan
Segmentation Performance U Net Segmentation Accuracy Interactive Segmentation Weight Re Mapping

November 11, 2021

Dense Unsupervised Learning for Video Segmentation
Nikita Araslanov, Simone Schaub-Meyer, Stefan Roth
Unsupervised Learning Segmentation Task Convolutional Layer Segmentation Accuracy Video Segmentation Dense Self Supervised Learning

November 3, 2021

Continual Adaptation of Semantic Segmentation using Complementary 2D-3D Data Representations
Jonas Frey, Hermann Blum, Francesco Milano, Roland Siegwart, Cesar Cadena
Semantic Segmentation 3d Representation Segmentation Accuracy Semantic Map Semantic Segmentation Network Continual Adaptation Complementary Representation

Segmentation Accuracy

Papers

PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers

HCFormer: Unified Image Segmentation with Hierarchical Clustering

PointInst3D: Segmenting 3D Instances by Points

Unsupervised Deep Learning Meets Chan-Vese Model

Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentation

Deep Multi-Branch Aggregation Network for Real-Time Semantic Segmentation in Street Scenes

Stage-Aware Feature Alignment Network for Real-Time Semantic Segmentation of Street Scenes

Topology-Preserving Segmentation Network: A Deep Learning Segmentation Framework for Connected Component

Mars Terrain Segmentation with Less Labels

Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree Species Classification Based on Explanation Methods

Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning

Towards Unsupervised Open World Semantic Segmentation

Interactive segmentation using U-Net with weight map and dynamic user interactions

Dense Unsupervised Learning for Video Segmentation

Continual Adaptation of Semantic Segmentation using Complementary 2D-3D Data Representations