Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence最新文献

Faster R-CNN based Automatic Parking Space Detection 更快的基于R-CNN的自动停车位检测

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426846

R. Patel, Praveen Meduri

引用次数: 6

Genetic Algorithm (GA)-Based Detection for Coded Partial-Response Channels 基于遗传算法的编码部分响应信道检测

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426844

Zhiliang Qin, Yu Qin, Yingying Li

引用次数: 0

Knowledge-based Deep Reinforcement Learning for Train Automatic Stop Control of High-Speed Railway 基于知识的深度强化学习在高速铁路列车自动停车控制中的应用

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426833

Yujun Cui, Guohua Zhang, Wei Dong, Xinya Sun, Weihua Yang

引用次数: 1

Alternation of Restarting Automata 重新启动自动机

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426851

Qichao Wang, Yongming Li, Xiaoyin Chen

引用次数: 0

Data Mining of Agricultural Software and Suggestions 农业软件数据挖掘及建议

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426841

Zheng-Ming Gao, Juan Zhao, Yu-Rong Hu

引用次数: 0

Split and Attentive-Aggregated Learnable Shift Module for Video Action Recognition 视频动作识别的分割和注意力聚合可学习移位模块

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426836

Xiao Wu, Qingge Ji

{"title":"Split and Attentive-Aggregated Learnable Shift Module for Video Action Recognition","authors":"Xiao Wu, Qingge Ji","doi":"10.1145/3426826.3426836","DOIUrl":"https://doi.org/10.1145/3426826.3426836","url":null,"abstract":"Existing approaches for video action recognition using convolutional neural network (CNN) usually suffer from the trade-off between accuracy and complexity. On the one hand, the 2D CNNs have difficulty in modeling the long-term temporal dependencies though they are computationally cheap. On the other hand, 3D CNNs have the ability to perceive temporal cues however lead to a high computational cost. In this paper, we propose a generic building block named Split and Attentive-aggregated Learnable Shift Module (SALSM) which has capacity of modeling spatiotemporal representations while maintain the complexity of the 2D CNN. Specifically, we split the input tensor into multiple groups, and conduct adaptive shift operations by applying the learnable shift kernels for different channels of each group along time dimension, so that the spatiotemporal information from neighboring frames can be mingled with 2D convolutions. The output feature maps of each group are integrated together with attention mechanism. With SALSM plugged in, the 2D CNN is enhanced to handle temporal information and become a highly efficient spatiotemporal feature extractor with little parameters and computational cost. We conduct ablation experiments to verify the effectiveness of our method, and our proposed SALSM achieves competitive or even better results over the state-of-the-art methods on several benchmark datasets.","PeriodicalId":202857,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130561954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Covariance Matrix Adaptation Evolution Strategy Based on Cooperative Co-Evolutionary Framework Using Delta Grouping for Large-Scale Dynamic Economic Dispatch 大规模动态经济调度中基于协同进化框架的协方差矩阵自适应进化策略

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426849

Qun Niu, Likun Wang, Ming-Sian You

引用次数: 0

Estimating the Number of Clusters via Proportional Chinese Restaurant Process 基于比例中餐馆过程的聚类数量估计

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426840

Yingying Wen, Hangjin Jiang, Jianwei Yin

引用次数: 0

The Multi-Task Time-Series Graph Network for Traffic Congestion Prediction 交通拥堵预测的多任务时序图网络

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426831

Lianliang Chen

引用次数: 1

A Review on Industrial Surface Defect Detection Based on Deep Learning Technology 基于深度学习技术的工业表面缺陷检测研究进展

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426832

Shengxiang Qi, Jiarong Yang, Z. Zhong

{"title":"A Review on Industrial Surface Defect Detection Based on Deep Learning Technology","authors":"Shengxiang Qi, Jiarong Yang, Z. Zhong","doi":"10.1145/3426826.3426832","DOIUrl":"https://doi.org/10.1145/3426826.3426832","url":null,"abstract":"In recent years, with the rapid development of deep learning, computer vision technology based on convolutional neural network (CNN) is widely used in industrial fields. At present, surface defect detection by machine vision is one of the most mature applications of CNN in industry. This paper provides a comprehensive overview of deep learning in the field. First of all, we briefly introduce the major tasks of CNN in computer vision researches, including image classification, object detection, edge detection and image segmentation, which are frequently used techniques in surface defect inspection. After that, we describe in detail the applications of computer vision based on CNN models in a variety of industrial scenarios for surface defect detection tasks, which mainly cover the steel surface defect inspection, magnetic tile surface defect inspection, rail surface defect inspection, screen surface detect inspection, solar cell surface defect inspection, and some others. As an emerging representative of artificial intelligence technology, we believe that deep learning will gradually become one of the mainstream technologies for industrial vision in the future. Accordingly, this paper aims to present a reference and guidance for researchers in industry to apply the advanced technology of deep learning.","PeriodicalId":202857,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127815725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20