2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)最新文献_第3页

Zero-Shot Stance Detection via Sentiment-Stance Contrastive Learning 基于情绪-姿态对比学习的零射击姿态检测

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00044

Jiaying Zou, Xuechen Zhao, Feng Xie, Bin Zhou, Zhong Zhang, Lei Tian

{"title":"Zero-Shot Stance Detection via Sentiment-Stance Contrastive Learning","authors":"Jiaying Zou, Xuechen Zhao, Feng Xie, Bin Zhou, Zhong Zhang, Lei Tian","doi":"10.1109/ICTAI56018.2022.00044","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00044","url":null,"abstract":"Zero-shot stance detection (ZSSD) is an important research problem that requires algorithms to have good stance detection capability even for unseen targets. In general, stance features can be grouped into two types: target-invariant and target-specific. Target-invariant features express the same stance regardless of the targets they are associated with, and such features are general and transferable. On the contrary, target-specific features will only be directly associated with specific targets. Therefore, it is crucial to effectively mine target-invariant features in texts in ZSSD. In this paper, we develop a method based on contrastive learning to mine certain transferable target-invariant expression features in texts from two dimensions of sentiment and stance and then generalize them to unseen targets. Specifically, we first grouped all texts into several types in terms of two orthogonal dimensions: sentiment polarity and stance polarity. Then we devise a supervised contrastive learning-based strategy to capture each type's common and transferable expressive features. Finally, we fuse the above-mentioned expressive features with the semantic features of the original texts about specific targets to deal with the stance detection for unseen targets. Extensive experiments on three benchmark datasets show that our proposed model achieves the state-of-the-art performance on most datasets. Code and other resources are available on GitHub11https://github.com/zoujiaying1995/sscl-project.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"15 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":"132200191","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

Explicit History Selection for Conversational Question Answering 会话式问答的显式历史选择

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00212

Zhiyuan Zhang, Qiaoqiao Feng, Yujie Wang

引用次数: 0

MoGoCo-@Net: Discrete Multi-Objective Grey Wolf Optimization for Discovering Community Structures in Attributed Networks MoGoCo-@Net:用于发现属性网络中社区结构的离散多目标灰狼优化

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00174

Mehdi Azaouzi, L. Romdhane

{"title":"MoGoCo-@Net: Discrete Multi-Objective Grey Wolf Optimization for Discovering Community Structures in Attributed Networks","authors":"Mehdi Azaouzi, L. Romdhane","doi":"10.1109/ICTAI56018.2022.00174","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00174","url":null,"abstract":"In this paper, we deal with the problem of community detection in attributed networks. The multi-objective grey wolf optimizer is adopted in order to discover the optimal partition with multiple objectives for the first time. This paper presents MoGoCo-@Net, a novel multiQbjective discrete grey wolf optimizatlon algorithm to solve the community detection-problem in the @ttributed networks. To fully exploit the topology structure and node attribute of vertices at each time step, we introduce two new criteria and maximize them simultaneously. Moreover, MoGoCo-@Net used opposition-based learning and improved label propagation technique by combining the node's attributes with the network topology for fast and effective initialization. Then, MoGoCo-@Net redefined the social hierarchy and the hunting behavior of grey wolves in discretization. Next, a multi-individual mutation operation is adopted as an evolutionary operation. In our experiments, various benchmark attributed networks are used to compare with some state-of-the-art methods. The experimental results show that the proposed algorithm performs favorably against the compared methods.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"8 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":"117181131","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

Imitation Learning-Based Drone Motion Planning in Dense Obstacle Scenarios 密集障碍物场景下基于模仿学习的无人机运动规划

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00126

Ji Wang, Ziyue Hou, Long Zhang, Wei Li, Zhongxue Gan

引用次数: 0

RITNet: A Rotation Invariant Transformer based Network for Point Cloud Registration RITNet:一种基于旋转不变变压器的点云配准网络

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00096

Min Yang, Yaochen Li, Su Wang, Shaohan Yang, Hujun Liu

引用次数: 0

In-air Handwriting System Based on Improved YOLOv5 algorithm and Monocular Camera 基于改进YOLOv5算法和单目摄像机的空中手写系统

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00145

Minghong Ye, Xiwen Qu, Jun Huang, Xuangou Wu

{"title":"In-air Handwriting System Based on Improved YOLOv5 algorithm and Monocular Camera","authors":"Minghong Ye, Xiwen Qu, Jun Huang, Xuangou Wu","doi":"10.1109/ICTAI56018.2022.00145","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00145","url":null,"abstract":"In-air handwriting is a new and more humanized human-computer interaction way. The existing in-air handwriting systems are mainly based on three-dimensional sensors, which are expensive, too large and not conducive for integration and application promotion. To solve this problem, this paper proposes a new in-air handwriting system using cheap and portable monocular camera which allows users writing freely in the air. Additionally we develop an end-to-end fingertip detection algorithm based on improved YOLOv5 algorithm to form in-air handwritten characters. Concretely, we first build a fingertip images dataset. After preprocessing and fingertip labeling, we use the dataset to train the improved YOLOv5 model, and then use the trained model to detect the coordinates of the fingertip in each video frame. After that, we connect the coordinates of the fingertip of each frame to form the character, and finally utilize the classifiers to recognize characters. The experimental results show that proposed in-air handwriting system allows user write freely in the air, and can obtain over 92 % in fingertip detection and character recognition.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"16 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":"128700555","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

Fully Parameterized Dueling Mixing Distributional Q-Leaning for Multi-Agent Cooperation 多智能体合作的全参数化决斗混合分布q学习

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00175

Qianhui Wang, Xinzhi Wang, Mingke Gao, Xiangfeng Luo, Y. Li, Han Zhang

{"title":"Fully Parameterized Dueling Mixing Distributional Q-Leaning for Multi-Agent Cooperation","authors":"Qianhui Wang, Xinzhi Wang, Mingke Gao, Xiangfeng Luo, Y. Li, Han Zhang","doi":"10.1109/ICTAI56018.2022.00175","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00175","url":null,"abstract":"Multi-agent reinforcement learning (MARL) has been applied to many multi-agent team tasks, such as multi-robot swarm control. Distributional Value Function Factorization (DFAC) follows the Distributional-Individual-Global-Max (DIGM) principle, which forces the individual's optimal action to be in accordance with the optimal joint action at all times. However, this principle leads to grade inflation, which limits agents to exploring better strategies than before. We focus on this issue and propose a novel MARL method named dueling mixing distributional Q-learning with fully parameters (FDMIX). Firstly, a parametric individual value network generates an individual distribution function and a utility value function, while the fractions are obtained through a fraction proposal network. Secondly, the conversion mixing network obeys a new advantage-based DIGM principle to generate a joint distribution action value based on the global state. Finally, we incorporate an N-step return-based loss function to achieve stable and efficient training. Our extensive tests on the multiple-particle environment and StarCraft II show that our method performs better than state-of-the-art algorithms noticeably.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"20 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":"127281409","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

Half Title Page 半页标题

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ictai56018.2022.00001

引用次数: 0

A Novel Multi-Frequency Coordinated Module for SAR Ship Detection 一种新型多频协调SAR舰船探测模块

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00124

Chenchen Qiao, Fei Shen, Xuejun Wang, Ruixin Wang, Fang Cao, Sixian Zhao, Chang Li

{"title":"A Novel Multi-Frequency Coordinated Module for SAR Ship Detection","authors":"Chenchen Qiao, Fei Shen, Xuejun Wang, Ruixin Wang, Fang Cao, Sixian Zhao, Chang Li","doi":"10.1109/ICTAI56018.2022.00124","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00124","url":null,"abstract":"Synthetic aperture radar (SAR) ships have rich multi-frequency information, however, existing SAR ship detection methods mostly only consider high-frequency information, ignoring other frequency features and structured relationships. For that, a novel plug-and-play Multi-Frequency Coordinated (MFC) module is developed for SAR ship detection. Specifically, the proposed MFC consists of the two key submodules, i.e., Multi-Frequency Aggregate (MFA) and Frequency Response (FR). First, MFA is used to refine the different frequency feature maps along the channel dimension and Discrete Cosine Transform (DCT) bases to solve the problem of dense multi-target SAR ship detection. Then, FR is introduced to select the one with a better response from multi-frequency and boost the significant features of ship targets and suppress interference of surroundings. Lastly, we develop a YOLOv5s-MFC by embedding the MFC for ship detection. Extensive experiments on three large-scale ship datasets (SSDD, HRSID, and LS-SSDD-v1.0) demonstrate that the proposed YOLOv5s-MFC is superior to state-of-the-art ship detection approaches.","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":"130713367","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

EMAS: Efficient Meta Architecture Search for Few-Shot Learning EMAS:高效元架构搜索的少镜头学习

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00099

Dongkai Liu, Jiaxing Li, Honglong Chen, Baodi Liu, Xiaoping Lu, Weifeng Liu

{"title":"EMAS: Efficient Meta Architecture Search for Few-Shot Learning","authors":"Dongkai Liu, Jiaxing Li, Honglong Chen, Baodi Liu, Xiaoping Lu, Weifeng Liu","doi":"10.1109/ICTAI56018.2022.00099","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00099","url":null,"abstract":"With the progress of few-shot learning, it has been scaled to many domains in the real world which have few labeled data, such as image classification and object detection. Many efforts for data embedding and feature combination have been made by designing a fixed neural architecture that can also be extended to variable and adaptive neural architectures for better performance. Recent works leverage neural architecture search technique to automatically design networks for few-shot learning but it requires vast computation costs and GPU memory requirements. This work introduces EMAS, an efficient method to speed up the searching process for few-shot learning. Specifically, we build a supernet to combine all candidate operations and then adopt gradient-based methods to search. Instead of training the whole supernet, we adopt Gumbel reparameterization technique to sample and activate a small subset of operations. EMAS handles a single path in a novel task adapted with just a few steps and time. A novel task only needs to learn fewer parameters and compute less content. During meta-testing, the task can well adapt to the network architecture although only with a few iterations. Empirical results show that EMAS yields a fair improvement in accuracy on the standard few-shot classification benchmark and is five times smaller in time.","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":"130225584","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