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Multiscale Feature-Clustering-Based Fully Convolutional Autoencoder for Fast Accurate Visual Inspection of Texture Surface Defects.

, , , and . IEEE Trans Autom. Sci. Eng., 16 (3): 1450-1467 (2019)

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Anomaly Composition and Decomposition Network for Accurate Visual Inspection of Texture Defects., , and . IEEE Trans. Instrum. Meas., (2022)An Anomaly Feature-Editing-Based Adversarial Network for Texture Defect Visual Inspection., , , and . IEEE Trans. Ind. Informatics, 17 (3): 2220-2230 (2021)Multiscale Feature-Clustering-Based Fully Convolutional Autoencoder for Fast Accurate Visual Inspection of Texture Surface Defects., , , and . IEEE Trans Autom. Sci. Eng., 16 (3): 1450-1467 (2019)Autolabeling-Enhanced Active Learning for Cost-Efficient Surface Defect Visual Classification., , , and . IEEE Trans. Instrum. Meas., (2021)Semantics-Consistent Feature Search for Self-Supervised Visual Representation Learning., , , , , and . CoRR, (2022)Semantic-Aware Autoregressive Image Modeling for Visual Representation Learning., , and . AAAI, page 4925-4933. AAAI Press, (2024)Robust visual tracking based on deep convolutional neural networks and kernelized correlation filters., , , , and . J. Electronic Imaging, 27 (2): 023008 (2018)Semantic-Aware Autoregressive Image Modeling for Visual Representation Learning., , and . CoRR, (2023)DropPos: Pre-Training Vision Transformers by Reconstructing Dropped Positions., , , , , and . CoRR, (2023)Semantics-Consistent Feature Search for Self-Supervised Visual Representation Learning., , , , and . ICCV, page 16053-16062. IEEE, (2023)