2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)最新文献_第9页

On Domain Generalization for Batched Prediction: the Benefit of Contextual Adversarial Training 批量预测的领域泛化:上下文对抗训练的好处

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00091

Chune Li, Yongyi Mao, Richong Zhang

引用次数: 0

A Convolutional Neural Network Based Infrared-Visible Image Fusion Method and its Application in Aerospace Measurement and Control 基于卷积神经网络的红外-可见光图像融合方法及其在航天测控中的应用

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00133

L. Zhang, Zhen-hua Chen, Jinqian Tao, Kangyi Zhang, Zhaodun Huang, Hao Ding

{"title":"A Convolutional Neural Network Based Infrared-Visible Image Fusion Method and its Application in Aerospace Measurement and Control","authors":"L. Zhang, Zhen-hua Chen, Jinqian Tao, Kangyi Zhang, Zhaodun Huang, Hao Ding","doi":"10.1109/ICTAI56018.2022.00133","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00133","url":null,"abstract":"In aerospace measurement and control missions, optical equipment collects infrared images and visible images with different characteristics individually. To acquire better image quality, image fusion method is studied in this paper. Firstly, based on the idea of dense connection in image classification, a feature encoding module is designed and the resolution of the feature map is kept unchanged to preserve the position information. Secondly, a image reconstruction module based on the symmetrical reconstruction in semantic segmentation is designed to fully merge the high-level and low-level feature. Then, a lightweight encoding-feature fusion-decoding image fusion neural network with only 9 CNN layers is proposed. The experimental results demonstrated that our proposed method achieve great performance improvement whether in subjective or objective evaluation. Besides, a real-time video capture and fusion system is built based on the Black Magic video capture card Declink Duo 2 and the high performance inference toolkit tensorRT. And the built system is successfully deployed in many aerospace measurement and control missions.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115159299","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

Learning an Interpretable Learning Rate Schedule via the Option Framework 通过选项框架学习可解释的学习率表

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00111

Chaojing Yao

{"title":"Learning an Interpretable Learning Rate Schedule via the Option Framework","authors":"Chaojing Yao","doi":"10.1109/ICTAI56018.2022.00111","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00111","url":null,"abstract":"Learning rate is a common and important hyperparameter in many gradient-based optimizers which are used for training machine learning models. Heuristic handcrafted learning rate schedules (LRSs) can work in many practical situations, but their design and tuning is a tedious work, and there is no guarantee that a given handcrafted LRS matches a given problem. Many works have been dedicated to automatically learning an LRS from the training dynamics of the optimization problem, but most of them share a common deficit: they borrow the algorithms designed elsewhere as methods for automatic outer-training, but those methods often lack interpretability in the context of learning an LRS. In this paper, we leverage the option framework, a generalization to the common rein-forcement learning framework, to automatically learn an LRS based on the dynamics of optimization, which takes the idea of temporal abstraction as an underlying interpretation. To meet the requirements of LLRS, the RL state is designed as consisting of the global state and the per-parameter state. We propose a policy architecture which processes these two parts according to their respective structures, and combines them to yield the input for functional computation. Experiments are carried out on classic machine learning tasks and test functions for numerical optimization to demonstrate the effectiveness and the interpretability of our method.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114990863","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 Lipreading Model Based on Fine-Grained Global Synergy of Lip Movement 一种基于唇动细粒度全局协同的唇读模型

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00130

Baosheng Sun, Dongliang Xie, Dawei Luo, Xiaojie Yin

{"title":"A Lipreading Model Based on Fine-Grained Global Synergy of Lip Movement","authors":"Baosheng Sun, Dongliang Xie, Dawei Luo, Xiaojie Yin","doi":"10.1109/ICTAI56018.2022.00130","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00130","url":null,"abstract":"Lipreading is a type of speech recognition based on visual information. It is instructive to design a lipreading model according to the lip movement law. Algorithms in the field of computer vision cannot fully satisfy the characteristics of lipreading, and direct use does not necessarily improve the performance of lipreading. In this paper, we propose that lipreading has fine-grained global synergy by comparing other computer vision tasks and analyzing lip muscle motion patterns. To address this feature, we propose a tailored model and name it Fine-Grained Global Synergy Lipreading (FGSLip). Our model aims to make features synergistic to improve lipreading performance. We introduce global features to represent the overall characteristics of the lip, and local features to learn coarse-grained and fine-grained correlations between features. Then, diffusion and fusion methods are used to make the local features and global features synergistic. Based on the above, several different feature extraction structures are constructed to demonstrate the fine-grained global synergy of lipreading. To verify the effectiveness of the proposed model, extensive experiments are conducted on the laboratory record dataset ICSLR and the public dataset CMLR, and the experimental results show that the proposed method can effectively improve the accuracy of lipreading.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132892644","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

Using Physiological Metrics to Improve Reinforcement Learning for Autonomous Vehicles 利用生理指标改进自动驾驶汽车的强化学习

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00186

Michael Fleicher, Oren Musicant, A. Azaria

{"title":"Using Physiological Metrics to Improve Reinforcement Learning for Autonomous Vehicles","authors":"Michael Fleicher, Oren Musicant, A. Azaria","doi":"10.1109/ICTAI56018.2022.00186","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00186","url":null,"abstract":"Thanks to recent technological advances Autonomous Vehicles (AVs) are becoming available at some locations. Safety impacts of these devices have, however, been difficult to assess. In this paper we utilize physiological metrics to improve the performance of a reinforcement learning agent attempting to drive an autonomous vehicle in simulation. We measure the performance of our reinforcement learner in several aspects, including the amount of stress imposed on potential passengers, the number of training episodes required, and a score measuring the vehicle's speed as well as the distance successfully traveled by the vehicle, without traveling off-track or hitting a different vehicle. To that end, we compose a human model, which is based on a dataset of physiological metrics of passengers in an autonomous vehicle. We embed this model in a reinforcement learning agent by providing negative reward to the agent for actions that cause the human model an increase in heart rate. We show that such a “passenger-aware” reinforcement learner agent does not only reduce the stress imposed on hypothetical passengers, but, quite surprisingly, also drives safer and its learning process is more effective than an agent that does not obtain rewards from a human model.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132976920","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

Vehicle Damage Detection based on MD R-CNN 基于MD R-CNN的车辆损伤检测

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00119

Yuxin Chen, Hua Yuan, Shoubin Dong, Jinbo Peng

引用次数: 0

RIA: A Reversible Network-based Imperceptible Adversarial Attack RIA:一种基于可逆网络的难以察觉的对抗性攻击

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00152

Fanxiao Li, Renyang Liu, Zhenli He, Song Gao, Yunyun Dong, Wei Zhou

{"title":"RIA: A Reversible Network-based Imperceptible Adversarial Attack","authors":"Fanxiao Li, Renyang Liu, Zhenli He, Song Gao, Yunyun Dong, Wei Zhou","doi":"10.1109/ICTAI56018.2022.00152","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00152","url":null,"abstract":"The robustness and security of deep neural network (DNN) models have received much attention in recent years. In-depth research on adversarial example generation methods that make DNN models make wrong judgments and decisions will facilitate further research on more comprehensive and practical adversarial defense methods. Most existing adversarial example generation methods focus too much on attack performance and design adversarial noise at the pixel level, resulting in the generated adversarial examples with redundant noise and evident perturbations. In this paper, we try to find the well-designed perturbations at the feature-level and propose a novel deep reversible network-based imperceptible adversarial examples generation method called RIA. Experimental results show that RIA can obtain more natural adversarial examples without losing attack performance and reducing redundant noise based on well-designed feature maps. To the best of our knowledge, in the white-box attack method research, this work is the first attempt to directly add perturbations to feature maps and use an reversible network to generate adversarial examples based on the perturbed feature maps.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124913493","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

Image Inpainting with Context Flow Network 使用上下文流网络的图像绘制

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00141

Jian Liu, Jiarui Xue, Juan Zhang, Ying Yang

{"title":"Image Inpainting with Context Flow Network","authors":"Jian Liu, Jiarui Xue, Juan Zhang, Ying Yang","doi":"10.1109/ICTAI56018.2022.00141","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00141","url":null,"abstract":"Image inpainting using deep and complicated convolutional neural networks(CNN) has recently produced outstanding results. Several researchers have considered employing large receptive fields and deep networks for long-distance information transfer to obtain semantically coherent inpainting results. As a side effect, these strategies would lead to the loss of detail and other artifacts. Motivated by the attention mechanism and sequence-to-sequence model, a novel convolution structure called context flow module is introduced into a coarse-to-fine two stages network, extracting information from distant regions without extra network layers or details loss. The context flow module in the refinement network can effectively gather both spatial and contextual data in the distance, and flow information to the next layer patch by patch. The coarse and refinement networks' backbones are encoder-decoder architecture stacked with gated and dilated convolutions. The refinement network encloses two extra elements: the context flow module and a feature-sharing space. The coarse network generates semantically consistent images with no gaps. The refinement network enhances the sharpness and enriches the details of the initial results. Moreover, a patch-based GAN is applied to stabilize training and generate semantically reasonable results. Experimental results show that our method excels at the performance of the state-of-the-art methods on faces(CelebA), buildings(Paris Street View), and natural images(Places2) datasets. The proposed context flow module can be easily integrated with any existing networks to improve their inpainting performance.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128865789","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

Detecting Backdoor Attacks on Deep Neural Networks Based on Model Parameters Analysis 基于模型参数分析的深度神经网络后门攻击检测

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00098

Mingyuan Ma, Hu Li, Xiaohui Kuang

{"title":"Detecting Backdoor Attacks on Deep Neural Networks Based on Model Parameters Analysis","authors":"Mingyuan Ma, Hu Li, Xiaohui Kuang","doi":"10.1109/ICTAI56018.2022.00098","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00098","url":null,"abstract":"With the introduction of the backdoor in deep neural networks (DNNs), much research focuses on backdoor attacks and defenses against DNNs. Since many DNN models are developed based on public datasets and pre-trained models often published by untrusted third parties, backdoors can be easily injected. The defender usually cannot access training data and does not know the target class or the triggers of the backdoor injected by the attacker. All these make it challenging to guarantee the security of decision guidance and support systems. In this paper, we proposed to detect backdoor attacks on DNNs based on model parameters analysis (MPA). We extracted and selected parameters related to the backdoor in the model's hidden layer and decision layer and trained the MPA classifier based on these parameters. We evaluated the effectiveness of the MPA classifier on various target models. The results show that the area under the receiver operating characteristic curve of the MPA classifier reaches 0.96 and 0.86 on the CIFAR10 and Troj target models, respectively. The MPA classifier improved the detection rate of backdoor attacks by 2%-6% compared with other advanced methods, with less prior knowledge and more relaxed constraints.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131426320","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

SICKNet: A Humor Detection Network Integrating Semantic Incongruity and Commonsense Knowledge 病态网络:一个整合语义不一致与常识知识的幽默检测网络

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00049

Penglong Huang, Xingwei Zeng, Jinta Weng, Ying Gao, Heyan Huang, Maobin Tang

引用次数: 0