2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)最新文献

A Survey of Object Detection Based on CNN and Transformer 基于CNN和Transformer的目标检测综述

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520732

Ershat Arkin, Nurbiya Yadikar, Yusnur Muhtar, K. Ubul

{"title":"A Survey of Object Detection Based on CNN and Transformer","authors":"Ershat Arkin, Nurbiya Yadikar, Yusnur Muhtar, K. Ubul","doi":"10.1109/PRML52754.2021.9520732","DOIUrl":"https://doi.org/10.1109/PRML52754.2021.9520732","url":null,"abstract":"The task of object detection is to find all the objects of interest in the image, and to determine their classifications and positions, which is one of the core problems in the field of computer vision. Since the emergence of AlexNet, convolutional neural networks have an absolute position in the field of computer vision, and the research on convolutional neural networks and algorithm structures has become more and more in-depth. Object detection algorithms can be roughly divided into two categories: candidate-based(two stage) and regression-based(one stage). The object detection algorithm based on the candidate area has high accuracy, but the structure is complex and the detection speed is slow. The regression-based object detection algorithm has a simple structure and fast detection speed. It has high application value in the field of real-time object detection, but the detection accuracy is relatively low. With the pursuit of the speed and accuracy of object detection, researchers try to apply mainstream methods in different fields. Therefore, recently Transformers in the NLP field has been used in computer vision, such as ViT, Swin Transformer, etc. It showed transformer-based models perform similar to or better than neural network algorithms, and pointed out new paths for researchers. This paper introduces classic neural networks, discusses the advantages and disadvantages of convolutional neural networks used in object detection algorithms, and introduces the latest innovative methods of Transformer used in computer vision. Finally, the difficulties, challenges and future development of convolutional neural networks and Transformers in object detection are considered.","PeriodicalId":429603,"journal":{"name":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114599606","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: 12

Intelligent Robot for Cleaning Garbage Based on OpenCV 基于OpenCV的智能垃圾清扫机器人

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520722

Shuang Pan, Zihui Xie, Xianao Yang, G. Lin, Yulian Jiang

引用次数: 0

A Review of Segmentation and Classification for Retinal Optical Coherence Tomography Images 视网膜光学相干断层扫描图像分割与分类研究进展

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520706

Zhijun Gao, Jian Wang, Xingle Wang, Xichao Dong, Yi Li

引用次数: 0

Effects of Pre-processing on the Performance of Transfer Learning Based Person Detection in Thermal Images 预处理对基于迁移学习的热图像人检测性能的影响

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520729

Noor Ul Huda, Rikke Gade, T. Moeslund

{"title":"Effects of Pre-processing on the Performance of Transfer Learning Based Person Detection in Thermal Images","authors":"Noor Ul Huda, Rikke Gade, T. Moeslund","doi":"10.1109/PRML52754.2021.9520729","DOIUrl":"https://doi.org/10.1109/PRML52754.2021.9520729","url":null,"abstract":"Thermal images have the property of identifying objects even in low light conditions. However, person detection in thermal is tricky, due to varying person representations depending upon the surrounding temperature. Three major polarities are commonly observed in these representations i.e., 1. person warmer than the background, 2. person colder than the background and 3. person’s body temperature is similar to background. In this work, we have studied and analyzed the performance of the detection network by using the data in its original form and by harmonizing the person representation in two ways i.e., dark persons in the light background and light persons in a darker background. The data passed to each testing scenario was first pre-processed using histogram stretching to enhance the contrast. The work also presents the method to separate the three kinds of images from thermal data. The analysis is performed on publicly available outdoor AAUPD-T and OSU-T datasets. Precision, recall, and F1 score is used to evaluate network performance. The results have shown that network performance is not enhanced by performing the mentioned pre-processing. Best results are obtained by using the data in its original form.","PeriodicalId":429603,"journal":{"name":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127698695","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: 3

Using Denoised LSTM Network for Tourist Arrivals Prediction 基于去噪LSTM网络的游客预测

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520695

Junke Wang, Peng Ge, Zhusheng Liu

引用次数: 1

An Efficient Co-location Pattern Approximation Algorithm Based on Clustering Branches 一种基于聚类分支的高效协同定位模式逼近算法

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520713

Duan Duanping

引用次数: 0

Super Resolution of Single Image Based on Multi Level Residual Self Attention Mechanism 基于多级残差自注意机制的单幅图像超分辨率

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520742

Junfeng Mao, Yaqi Hu

引用次数: 0

Damage Detection in Switch Rails via Machine Learning 基于机器学习的交换轨道损伤检测

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520705

Weixu Liu, Zhifeng Tang, Pengfei Zhang, Xiangxian Chen, Bin Yang

{"title":"Damage Detection in Switch Rails via Machine Learning","authors":"Weixu Liu, Zhifeng Tang, Pengfei Zhang, Xiangxian Chen, Bin Yang","doi":"10.1109/PRML52754.2021.9520705","DOIUrl":"https://doi.org/10.1109/PRML52754.2021.9520705","url":null,"abstract":"Switch rail is a weak but essential component of high-speed rail (HSR) systems. Due to aging and the potential of fatigue damage accumulation, it has an urgent requirement for damage detection. An automatic classification method of switch rail damage based on feature integration and machine learning is proposed. According to the characteristics of switch rail and guided wave, several features extracted from different signal processing domains (such as time domain, power spectrum domain and time-frequency domain) are proposed and defined to characterize the complexity of switch rail damage. A damage index is defined to eliminate the effects of various environmental and operational conditions. A feature selection method based on binary particle swarm optimization (BPSO) is proposed. This method uses a new fitness function to select the most damage-sensitive features, eliminate the irrelevant and redundant features, and improve the classification performance. The least-squares support-vector machine (LS-SVM) is adopted to build an automatic classification model to reduce the probability of artificial error diagnosis and improve the generalization ability. Finally, experiment on the switch rail foot is conducted to verify the proposed method. The results show that the method has the ability of damage identification, which is better than traditional methods.","PeriodicalId":429603,"journal":{"name":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115503597","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: 1

Efficient and Bias-aware Recommendation with Two-side Relevance for Implicit Feedback 基于隐性反馈的双向关联的高效、偏见感知推荐

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520701

Guanyu Lin, Lei Huang, Yuting Yin, Chengmin Zhang, Feng Zhu, Lingqi Kong, Zhiheng Li

{"title":"Efficient and Bias-aware Recommendation with Two-side Relevance for Implicit Feedback","authors":"Guanyu Lin, Lei Huang, Yuting Yin, Chengmin Zhang, Feng Zhu, Lingqi Kong, Zhiheng Li","doi":"10.1109/PRML52754.2021.9520701","DOIUrl":"https://doi.org/10.1109/PRML52754.2021.9520701","url":null,"abstract":"Today’s wide-spread recommendation is usually constructed based on implicit data such as click for easy collection but whether the no clicked data is negative feedback or unobserved positive feedback confuses the model construction. As a response, Relevance Matrix Factorization (Rel-MF) is recently proposed to tackle this problem as well as the missing-not-at-random (MNAR) problem ignored by previous studies. However, Rel-MF meets three problems: limited assumption (LA), negative square loss (NSL) and indiscriminate no click data (INCD). In this paper, we first get rid of Rel-MF’s limited assumption and establish a more general theory by incorporating a defined transformation function which captures the relevance level to our two-side relevance ideal loss, containing Rel-MF’s theory. To resolve the INCD problem and NSL problem, we introduce an adjusting variable and perform normalization, respectively, which is called Naive Solution with Normalization for Rel-MF (NRel-MF). But we then analytically discover that the clipped function proposed by Rel-MF meets the high variance problem. To overcome it, we design a power clipped function and further propose Improved Solution with Power Function for Rel-MF (PRel-MF). Besides, we also explore propensity score estimation from user and hybrid perspectives in contrast to Rel-MF’s sole item perspective. Finally, we also consider and address the computational problem caused by the Rel-MF’s non-sampling strategy. Empirical results verify the effectiveness of our solutions from both performance even in rare items and loss decrease. In broader perspective experiment, decent performance is seen in item perspective with fewer recommended items while in user perspective with more recommended items and hybrid perspective outperforms them in more situations.","PeriodicalId":429603,"journal":{"name":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127236619","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

How Does Chinese Segmentation Strategy Effect on Sentiment Analysis of Short Text? 汉语分词策略对短文本情感分析的影响

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520738

Qing Lei, Haifeng Li, Yanxi Chen

引用次数: 0