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Defect detection in textile fabrics with optimal Gabor filter and BRDPSO algorithm

Journal of Physics: Conference Series 2020年第1期1651卷

作者： Jiawei Zhang Yueyang Li Haichi Luo Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi 214122 China College of Internet of Things Engineering Jiangnan University Wuxi 214122 China

This paper presents an effective method that can detect fabric defects. The method utilizes the optimal Gabor filter and binary random drift particle swarm algorithm (BRDPSO) that can implement feature selection and parameter optimization synchronously. The parameters of 2D-Gabor filters are adjusted by quantum-behaved particle swarm optimization algorithm (QPSO) and the optimal Gabor filter is obtained. BRDPSO is used to select features on the original feature set and simultaneously optimize the parameters of the Isolation Forest (IF) classifier. Extensive experimental results indicate that the proposed method has effective detecting performance on the defect detection of textile fabric.

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Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning

arXiv

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arXiv 2022年

作者： Li, Yun Liu, Zhe Chang, Xiaojun McAuley, Julian Yao, Lina The School of Computer Science and Engineering University of New South Wales SydneyNSW2052 Australia The Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China The School of Computer Science and Engineering University of New South Wales SydneyNSW2052 Australia The Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia The School of Computer Science and Engineering University of California San Diego San DiegoCA United States CSIRO’s Data61 University of New South Wales SydneyNSW2052 Australia

Zero-Shot Learning (ZSL) aims to transfer classification capability from seen to unseen classes. Recent methods have proved that generalization and specialization are two essential abilities to achieve good performance in ZSL. However, focusing on only one of the abilities may result in models that are either too general with degraded classification ability or too specialized to generalize to unseen classes. In this paper, we propose an end-to-end network, termed as BGSNet, which equips and balances generalization and specialization abilities at the instance and dataset level. Specifically, BGSNet consists of two branches: the Generalization Network (GNet), which applies episodic meta-learning to learn generalized knowledge, and the Balanced Specialization Network (BSNet), which adopts multiple attentive extractors to extract discriminative features and achieve instance-level balance. A novel self-adjusted diversity loss is designed to optimize BSNet with redundancy reduced and diversity boosted. We further propose a differentiable dataset-level balance and update the weights in a linear annealing schedule to simulate network pruning and thus obtain the optimal structure for BSNet with dataset-level balance achieved. Experiments on four benchmark datasets demonstrate our model’s effectiveness. Sufficient component ablations prove the necessity of integrating and balancing generalization and specialization abilities. Copyright © 2022, The Authors. All rights reserved.

关键词： Zero-shot learning

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Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution

arXiv

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arXiv 2022年

作者： Li, Wenjie Li, Juncheng Gao, Guangwei Deng, Weihong Zhou, Jiantao Yang, Jian Qi, Guo-Jun The Intelligent Visual Information Perception Laboratory Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210046 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China The School of Communication and Information Engineering Shanghai University Shanghai200444 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210094 China The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China The Research Center for Industries of the Future The School of Engineering Westlake University Hangzhou310024 China OPPO Research SeattleWA98101 United States

Recently, Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks due to the ability of global feature extraction. However, the capabilities of Transformers that need to incorporate contextual information to extract features dynamically are neglected. To address this issue, we propose a lightweight Cross-receptive Focused Inference Network (CFIN) that consists of a cascade of CT Blocks mixed with CNN and Transformer. Specifically, in the CT block, we first propose a CNN-based Cross-Scale Information Aggregation Module (CIAM) to enable the model to better focus on potentially helpful information to improve the efficiency of the Transformer phase. Then, we design a novel Cross-receptive Field Guided Transformer (CFGT) to enable the selection of contextual information required for reconstruction by using a modulated convolutional kernel that understands the current semantic information and exploits the information interaction within different self-attention. Extensive experiments have shown that our proposed CFIN can effectively reconstruct images using contextual information, and it can strike a good balance between computational cost and model performance as an efficient model. Source codes will be available at https://***/IVIPLab/CFIN. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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