Multi-Task Classification of Sewer Pipe Defects and Properties using a Cross-Task Graph Neural Network Decoder
Author:
Affiliation:
1. Aalborg University,Visual Analysis and Perception (VAP) Laboratory,Denmark
2. Autonomous University of Barcelona,Computer Vision Center,Spain
Funder
Innovation Fund
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9706406/9706408/09706846.pdf?arnumber=9706846
Reference81 articles.
1. Deep multi-task representation learning: A tensor factorisation approach;yang;ICLR International Conference on Learning Representations,2017
2. PAD-Net: Multi-tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing
3. Automatic Detection and Classification of Sewer Defects via Hierarchical Deep Learning
4. Automated sewer pipe defect tracking in CCTV videos based on defect detection and metric learning
5. Robust Learning Through Cross-Task Consistency
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1. Attention‐guided multiscale neural network for defect detection in sewer pipelines;Computer-Aided Civil and Infrastructure Engineering;2023-03-07
2. Multi-scale hybrid vision transformer and Sinkhorn tokenizer for sewer defect classification;Automation in Construction;2022-12
3. CAFEN: A Correlation-Aware Feature Enhancement Network for Sewer Defect Identification;2022 21st International Symposium on Communications and Information Technologies (ISCIT);2022-09-27
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