Proceedings of the 2023 6th International Conference on Machine Vision and Applications最新文献_第2页

SARAF: Searching for Adversarial Robust Activation Functions SARAF:搜索对抗鲁棒激活函数

Proceedings of the 2023 6th International Conference on Machine Vision and Applications Pub Date : 2023-03-10 DOI: 10.1145/3589572.3589598

Maghsood Salimi, Mohammad Loni, M. Sirjani, A. Cicchetti, Sara Abbaspour Asadollah

{"title":"SARAF: Searching for Adversarial Robust Activation Functions","authors":"Maghsood Salimi, Mohammad Loni, M. Sirjani, A. Cicchetti, Sara Abbaspour Asadollah","doi":"10.1145/3589572.3589598","DOIUrl":"https://doi.org/10.1145/3589572.3589598","url":null,"abstract":"Convolutional Neural Networks (CNNs) have received great attention in the computer vision domain. However, CNNs are vulnerable to adversarial attacks, which are manipulations of input data that are imperceptible to humans but can fool the network. Several studies tried to address this issue, which can be divided into two categories: (i) training the network with adversarial examples, and (ii) optimizing the network architecture and/or hyperparameters. Although adversarial training is a sufficient defense mechanism, they suffer from requiring a large volume of training samples to cover a wide perturbation bound. Tweaking network activation functions (AFs) has been shown to provide promising results where CNNs suffer from performance loss. However, optimizing network AFs for compensating the negative impacts of adversarial attacks has not been addressed in the literature. This paper proposes the idea of searching for AFs that are robust against adversarial attacks. To this aim, we leverage the Simulated Annealing (SA) algorithm with a fast convergence time. This proposed method is called SARAF. We demonstrate the consistent effectiveness of SARAF by achieving up to 16.92%, 18.3%, and 15.57% accuracy improvement against BIM, FGSM, and PGD adversarial attacks, respectively, over ResNet-18 with ReLU AFs (baseline) trained on CIFAR-10. Meanwhile, SARAF provides a significant search efficiency compared to random search as the optimization baseline.","PeriodicalId":296325,"journal":{"name":"Proceedings of the 2023 6th International Conference on Machine Vision and Applications","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127493308","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

Employing Machine Learning and an OCR Validation Technique to Identify Product Category Based on Visible Packaging Features 利用机器学习和OCR验证技术识别基于可见包装特征的产品类别

Proceedings of the 2023 6th International Conference on Machine Vision and Applications Pub Date : 2023-03-10 DOI: 10.1145/3589572.3589589

Takorn Prexawanprasut, Lalita Santiworarak, Piyaporn Nurarak, Poom Juasiripukdee

引用次数: 0

Process Quality Prediction Algorithm of Multi output Workshop Based on ATT-CNN-TCN 基于ATT-CNN-TCN的多输出车间工艺质量预测算法

Proceedings of the 2023 6th International Conference on Machine Vision and Applications Pub Date : 2023-03-10 DOI: 10.1145/3589572.3589590

Bin Yi, Wenqiang Lin, Wenqi Li, Xiaohua Gao, Bing Zhou, Jun Tang

引用次数: 0

Research on Compact Quantum Classifier Based on Kernel Method 基于核方法的紧凑量子分类器研究

Proceedings of the 2023 6th International Conference on Machine Vision and Applications Pub Date : 2023-03-10 DOI: 10.1145/3589572.3589592

Ruihong Jia, Guang Yang, Min Nie, Yun Zhang

{"title":"Research on Compact Quantum Classifier Based on Kernel Method","authors":"Ruihong Jia, Guang Yang, Min Nie, Yun Zhang","doi":"10.1145/3589572.3589592","DOIUrl":"https://doi.org/10.1145/3589572.3589592","url":null,"abstract":"Kernel method is widely used in machine learning. At present, the connection between kernel methods and quantum computing has been gradually established, which provides a new algorithm idea for the field of quantum machine learning. Research shows that the construction of minimized quantum circuits can be reliably performed on Noisy Intermediate-Scale Quantum (NISQ) devices. This paper proposes a compact quantum classifier based on kernel method. By introducing the compact amplitude encoding, the data label of the phase corresponding to the quantum state is encoded. Compared with the proposed classifier based on quantum kernel method, it can reduce 2 quantum registers, further reduce the circuit depth, and thus reduce the algorithm complexity. The double qubit measurement is simplified to single qubit measurement. In addition, this model achieves the optimal variance in quantum circuit parameters, which can effectively save computational resources. Experimental simulation shows that the expected value measurement in the proposed classifier model is closer to the theoretical value, and the classification accuracy is more accurate. At the same time, the system model has low entanglement, which can effectively reduce the cost of the whole preparation.","PeriodicalId":296325,"journal":{"name":"Proceedings of the 2023 6th International Conference on Machine Vision and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129147980","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

Integrating User Gaze with Verbal Instruction to Reliably Estimate Robotic Task Parameters in a Human-Robot Collaborative Environment 基于用户注视和语言指令的人机协作环境下机器人任务参数可靠估计

Proceedings of the 2023 6th International Conference on Machine Vision and Applications Pub Date : 2023-03-10 DOI: 10.1145/3589572.3589580

S. K. Paul, M. Nicolescu, M. Nicolescu

{"title":"Integrating User Gaze with Verbal Instruction to Reliably Estimate Robotic Task Parameters in a Human-Robot Collaborative Environment","authors":"S. K. Paul, M. Nicolescu, M. Nicolescu","doi":"10.1145/3589572.3589580","DOIUrl":"https://doi.org/10.1145/3589572.3589580","url":null,"abstract":"As robots become more ubiquitous in our daily life, it has become very important to extract task and environmental information through more natural, meaningful, and easy-to-use interaction interfaces. Not only this helps the user to adapt to (thus trust) a robot in a collaborative environment, it can supplement the core sensory information, helping the robot make reliable decisions. This paper presents a framework that combines two natural interaction interfaces: speech and gaze to reliably infer the object of interest and the robotic task parameters. The gaze estimation module utilizes pre-defined 3D facial points and matches them to a set of extracted estimated 3D facial landmarks of the users from 2D images to infer the gaze direction. Subsequently, the verbal instructions are passed through a deep learning model to extract the information relevant to a robotic task. These extracted task parameters from verbal instructions along with the estimated gaze directions are combined to detect and/or disambiguate objects in the scene to generate the final task configurations. The proposed framework shows very promising results in integrating the relevant task parameters for the intended robotic tasks in different real-world interaction scenarios.","PeriodicalId":296325,"journal":{"name":"Proceedings of the 2023 6th International Conference on Machine Vision and Applications","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124519124","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

Quality metrics prediction in process manufacturing based on CNN-LSTM transfer learning algorithm 基于CNN-LSTM迁移学习算法的过程制造质量指标预测

Proceedings of the 2023 6th International Conference on Machine Vision and Applications Pub Date : 2023-03-10 DOI: 10.1145/3589572.3589591

Bin Yi, Jun Tang, Wenqiang Lin, Xiaohua Gao, Bing Zhou, Junjun Fang, Yulei Gao, Wenqi Li

{"title":"Quality metrics prediction in process manufacturing based on CNN-LSTM transfer learning algorithm","authors":"Bin Yi, Jun Tang, Wenqiang Lin, Xiaohua Gao, Bing Zhou, Junjun Fang, Yulei Gao, Wenqi Li","doi":"10.1145/3589572.3589591","DOIUrl":"https://doi.org/10.1145/3589572.3589591","url":null,"abstract":"The prediction of production process quality indicators plays an important role in product quality and production scheduling in process industries. In order to exploit the effective information contained in the massive process data, improve the prediction accuracy of production process quality indicators and apply to the changes of processing conditions, a hybrid model quality indicator migration learning prediction method based on convolutional network (CNN) and long-short-term memory (LSTM) is proposed. Massive amounts of historical process data, operational data and date data were constructed into a continuous feature matrix with a time-sliding window. The feature vectors are first extracted using CNN, and the feature vectors are constructed in a time-series sequence and used as input data for the LSTM network. Then the LSTM network is used for quality index prediction. In this process, migration learning strategy is introduced, which reduced the training time while ensuring the training accuracy. Finally, the correctness and effectiveness of the proposed method is verified by using the process data of a tobacco factory microtobacco cutting test line as a case object.","PeriodicalId":296325,"journal":{"name":"Proceedings of the 2023 6th International Conference on Machine Vision and Applications","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130045792","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

Multi-temporal process quality prediction based on graph neural network 基于图神经网络的多时间过程质量预测

Proceedings of the 2023 6th International Conference on Machine Vision and Applications Pub Date : 2023-03-10 DOI: 10.1145/3589572.3589599

Bin Yi, Wenqi Li, Jun Tang, Xiaohua Gao, Bing Zhou, Xiaoli Xu, Peng Qin, Wenqiang Lin

引用次数: 0

Detection of Fibrillatory Episodes in Atrial Fibrillation Rhythms via Topology-informed Machine Learning 通过拓扑信息的机器学习检测心房颤动节律中的纤颤发作

Proceedings of the 2023 6th International Conference on Machine Vision and Applications Pub Date : 2023-03-10 DOI: 10.1145/3589572.3589576

Paul Samuel P. Ignacio

{"title":"Detection of Fibrillatory Episodes in Atrial Fibrillation Rhythms via Topology-informed Machine Learning","authors":"Paul Samuel P. Ignacio","doi":"10.1145/3589572.3589576","DOIUrl":"https://doi.org/10.1145/3589572.3589576","url":null,"abstract":"Effective and efficient methods for diagnosing cardiac conditions remain of significant importance and relevance in clinical cardiology. As such, advances in machine- and deep-learning technologies pave the way to high throughput approaches to automated classification of cardiac abnormalities. While there is rich literature on ECG-based classification of cardiac conditions, particularly on diagnosing Atrial Fibrillation, there is a dearth on algorithms that can effectively measure the onset and offset of atrial fibrillation events within an ECG. In this work, we show that an off-the-shelf machine learning algorithm can be trained on mathematically-computable shape signatures embedded within the local topology of ECGs to identify fibrillatory episodes in ECGs of AF patients. More precisely, we show that a topology-informed machine learning algorithm can accurately classify segments within an ECG as either resembling an atrial fibrillation event or not. Furthermore, we show that based on the model-provided classification of segments, a simple criterion may be used to determine whether the AF rhythm is paroxysmal or persistent.","PeriodicalId":296325,"journal":{"name":"Proceedings of the 2023 6th International Conference on Machine Vision and Applications","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115636831","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

Discrete Radial-Harmonic-Fourier Moments for Image Description 离散径向-谐波-傅里叶矩用于图像描述

Proceedings of the 2023 6th International Conference on Machine Vision and Applications Pub Date : 2023-03-10 DOI: 10.1145/3589572.3589600

Kejia Wang, Ziliang Ping, Y. Sheng

引用次数: 0

Road Lane Segmentation Using Vehicle Trajectory Tracking and Lane Demarcation Lines 基于车辆轨迹跟踪和车道分界线的道路车道分割

Proceedings of the 2023 6th International Conference on Machine Vision and Applications Pub Date : 2023-03-10 DOI: 10.1145/3589572.3589582

Adriel Isaiah V. Amoguis, Hernand Ang Hermida, G. J. B. Madrid, Gabriel Costes Marquez, Justin Opulencia Dy, Jose Gerardo Ortile Guerrero, J. Ilao

{"title":"Road Lane Segmentation Using Vehicle Trajectory Tracking and Lane Demarcation Lines","authors":"Adriel Isaiah V. Amoguis, Hernand Ang Hermida, G. J. B. Madrid, Gabriel Costes Marquez, Justin Opulencia Dy, Jose Gerardo Ortile Guerrero, J. Ilao","doi":"10.1145/3589572.3589582","DOIUrl":"https://doi.org/10.1145/3589572.3589582","url":null,"abstract":"As levels of road traffic congestion increase relative to population density, it is becoming increasingly necessary for traffic managers to have awareness of road situations in real-time to keep up with traffic management. There are already existing techniques and applications in computer vision that traffic managers use to collect real-time telemetry, such as but not limited to vehicle counting algorithms. However, these algorithms and applications may not be lane-aware. Enabling lane awareness to these systems allows them to be more granular, which enables more in-depth telemetry such as lane usage, driver pattern recognition, and anomaly detection, among others. Lane awareness in these systems are enabled by performing lane segmentation. This study investigates two approaches to this. The first approach uses vehicle trajectories to generate aggregated trajectory maps, which are then clustered to determine trajectory lane membership and to generate representative trajectories that describes the lane. On the other hand, the second approach takes an end-to-end method and uses road lane features such as demarcation lines to segment lanes. The first approach proved to be more viable as a lane segmentation algorithm compared to the second approach as it was able to segment lanes more reliably, given enough vehicle trajectories are present.","PeriodicalId":296325,"journal":{"name":"Proceedings of the 2023 6th International Conference on Machine Vision and Applications","volume":"228 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124524930","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