Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition最新文献_第9页

DOA Estimation Based on a Sparsity Pre-estimation Adaptive Matching Pursuit Algorithm 基于稀疏预估计自适应匹配跟踪算法的DOA估计

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581863

Huijing Dou, Dongxu Xie, W. Guo

引用次数: 0

Vehicle Re-identification Based on Multi-Scale Attention Feature Fusion 基于多尺度注意力特征融合的车辆再识别

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581813

Geyan Su, Zhonghua Sun, Kebin Jia, Jinchao Feng

引用次数: 0

Friend Relation Recognization Algorithm Based on The Campus Card Consumption 基于校园一卡通消费的朋友关系识别算法

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581883

Haopeng Zhang, Jinbo Yu, Mengyu Li, Yuchen Zhang, Yulong Ling, Xiao Zhang

{"title":"Friend Relation Recognization Algorithm Based on The Campus Card Consumption","authors":"Haopeng Zhang, Jinbo Yu, Mengyu Li, Yuchen Zhang, Yulong Ling, Xiao Zhang","doi":"10.1145/3581807.3581883","DOIUrl":"https://doi.org/10.1145/3581807.3581883","url":null,"abstract":"College students live alone without their parents and bear the influence of academics, life, personality, family, and other factors alone, which leads to the phenomenon of isolation and autism in some students. If this situation is not detected and resolved in time, it may cause serious consequences. This paper uses the consumption data of students to analyze the students' friendship situation. First, it examines the consumption data of the students' campus all-in-one cards and observes the consumption behaviors of the students from the three aspects of consumption time, consumption location, and consumption frequency. It is found that the more overlapping the trajectories of the consumption locations among students, the more likely there is a friendship between students. On this basis, this paper proposes a student friend discovery model, which further explores the social relationship between students from the perspective of multiple colleges, and can find both friend relationships and lonely students. The experimental results show that the excavated social relationships align with the actual situation.","PeriodicalId":292813,"journal":{"name":"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125129053","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

Human Action Recognition Based on Vision Transformer and L2 Regularization 基于视觉变换和L2正则化的人体动作识别

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581840

Qiliang Chen, Hasiqidalatu Tang, Jia-xin Cai

引用次数: 0

Gas flow meter anomaly data detection based on fused LOF-DBSCAN algorithm 基于融合LOF-DBSCAN算法的气体流量计异常数据检测

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581881

Xichao Yue, Chaoqun Wang, Yong Wang, Le Chen, Weifei Wang, Yuhang Lei

{"title":"Gas flow meter anomaly data detection based on fused LOF-DBSCAN algorithm","authors":"Xichao Yue, Chaoqun Wang, Yong Wang, Le Chen, Weifei Wang, Yuhang Lei","doi":"10.1145/3581807.3581881","DOIUrl":"https://doi.org/10.1145/3581807.3581881","url":null,"abstract":"Anomaly detection for gas flowmeter data is one of the important means to improve the reliability of fair trade of natural gas transmission and distribution. However, the field environment of natural gas in the industrial scene has the characteristics of complex anomaly categories and difficult to distinguish some anomalies. At the same time, the traditional anomaly detection methods are difficult to accurately analyze the abnormal state for a period of time, and are easy to be disturbed by many factors. For example, although DBSCAN (density based spatial clustering of applications with noise) can cluster dense data sets of arbitrary shape, it will greatly affect the classification effect of data sets with uneven density, and the noise points will also interfere to a certain extent, resulting in the weakening of the ability of the algorithm to distinguish anomalies. LOF(local outliers factor) algorithm realizes outlier detection by calculating the local density deviation of a given data point relative to its neighborhood. In view of the above problems. A more accurate anomaly detection strategy is proposed. Firstly, the local anomaly factor algorithm is used to eliminate outliers with too large LOF value, so as to reduce the clustering effect of DBSCAN due to uneven density as much as possible. Experiments show that the clustering effect of this strategy is significantly improved compared with the traditional detection methods.","PeriodicalId":292813,"journal":{"name":"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114954624","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

VA-TransUNet: A U-shaped Medical Image Segmentation Network with Visual Attention 基于视觉注意的u型医学图像分割网络VA-TransUNet

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581826

Ting Jiang, Tao Xu, Xiaoning Li

{"title":"VA-TransUNet: A U-shaped Medical Image Segmentation Network with Visual Attention","authors":"Ting Jiang, Tao Xu, Xiaoning Li","doi":"10.1145/3581807.3581826","DOIUrl":"https://doi.org/10.1145/3581807.3581826","url":null,"abstract":"Abstract: Medical image segmentation is clinically important in medical diagnosis as it permits superior lesion detection in medical diagnosis to help physicians assist in treatment. Vision Transformer (ViT) has achieved remarkable results in computer vision and has been used for image segmentation tasks, but the potential in medical image segmentation remains largely unexplored with the special characteristics of medical images. Moreover, ViT based on multi-head self-attention (MSA) converts the image into a one-dimensional sequence, which destroys the two-dimensional structure of the image. Therefore, we propose VA-TransUNet, which combines the advantages of Transformer and Convolutional Neural Networks (CNN) to capture global and local contextual information and consider the features of channel dimensionality. Transformer based on visual attention is adopted, it is taken as the encoder, CNN is used as the decoder, and the image is directly fed into the Transformer. The key of visual attention is the large kernel attention (LKA), which is a depth-wise separable convolution that decomposes a large convolution into various convolutions. Experiment on Synapse of abdominal multi-organ (Synapse) and Automated Cardiac Diagnosis Challenge (ACDC) datasets demonstrate that we proposed VA-TransUNet outperforms the current the-state-of-art networks. The codes and trained models will be publicly and available at https://github.com/BeautySilly/VA-TransUNet.","PeriodicalId":292813,"journal":{"name":"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114283841","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

HSI-DETR: A DETR-based Transfer Learning from RGB to Hyperspectral Images for Object Detection of Live and Dead Cells: To achieve better results, convert models with the fewest changes from RGB to HSI. HSI- detr:基于der的从RGB到高光谱图像的迁移学习，用于活细胞和死细胞的目标检测:为了获得更好的结果，将RGB变化最小的模型转换为HSI。

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581822

Songxin Ye, Nanying Li, Jiaqi Xue, Yaqian Long, S. Jia

{"title":"HSI-DETR: A DETR-based Transfer Learning from RGB to Hyperspectral Images for Object Detection of Live and Dead Cells: To achieve better results, convert models with the fewest changes from RGB to HSI.","authors":"Songxin Ye, Nanying Li, Jiaqi Xue, Yaqian Long, S. Jia","doi":"10.1145/3581807.3581822","DOIUrl":"https://doi.org/10.1145/3581807.3581822","url":null,"abstract":"Traditional cell viability judgment methods are invasive and damaging to cells. Moreover, even under a microscope, it is difficult to distinguish live cells from dead cells by the naked eye alone. With the development of optical imaging technology, hyperspectral imaging is more and more widely used in various fields. Hyperspectral imaging is a non-contact optical technique that provides both spectral and spatial information in a single measurement. It becomes a fast, non-invasive option to differentiate between live and dead cells. In recent years, the rapid development of deep learning has provided a better way to distinguish the difference between living and dead cells through a large amount of data. However, it is often necessary to acquire large amounts of labeled data at an expensive cost to train models. This is more difficult to achieve on medical hyperspectral images. Therefore, in this paper, a new model called HSI-DETR is proposed to solve the above problem on the target detection task of live and dead cells, which is based on the detection transformer (DETR) model. The HSI-DETR model suitable for hyperspectral images (HSI) is proposed with minimal modification. Then, some parameters of DETR trained on RGB images are transferred to HSI-DETR trained on hyperspectral images. Compared to the general method, this method can train a better model with a small number of labeled samples. And compared to the DETR-R50, the AP50 of HSI-DETR-R50 has increased by 5.15%.","PeriodicalId":292813,"journal":{"name":"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131386339","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

Few-shot Object Detection via Refining Eigenspace 基于特征空间细化的少镜头目标检测

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581820

Yan Ouyang, Xinqing Wang, Honghui Xu, Ruizhe Hu, Faming Shao, Dong Wang

引用次数: 0

Swin Transformer with Multi-Scale Residual Attention for Semantic Segmentation of Remote Sensing Images 基于多尺度剩余注意的Swin变压器遥感图像语义分割

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581827

Yuanyang Lin, Da-han Wang, Yun Wu, Shunzhi Zhu

{"title":"Swin Transformer with Multi-Scale Residual Attention for Semantic Segmentation of Remote Sensing Images","authors":"Yuanyang Lin, Da-han Wang, Yun Wu, Shunzhi Zhu","doi":"10.1145/3581807.3581827","DOIUrl":"https://doi.org/10.1145/3581807.3581827","url":null,"abstract":"Semantic segmentation of remote sensing images usually faces the problems of unbalanced foreground-background, large variation of object scales, and significant similarity of different classes. The FCN-based fully convolutional encoder-decoder architecture seems to have become the standard for semantic segmentation, and this architecture is also prevalent in remote sensing images. However, because of the limitations of CNN, the encoder cannot obtain global contextual information, which is extraordinarily important to the semantic segmentation of remote sensing images. By contrast, in this paper, the CNN-based encoder is replaced by Swin Transformer to obtain rich global contextual information. Besides, for the CNN-based decoder, we propose a multi-level connection module (MLCM) to fuse high-level and low-level semantic information to help feature maps obtain more semantic information and use a multi-scale upsample module (MSUM) to join the upsampling process to recover the resolution of images better to get segmentation results preferably. The experimental results on the ISPRS Vaihingen and Potsdam datasets demonstrate the effectiveness of our proposed method.","PeriodicalId":292813,"journal":{"name":"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134176559","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

An Active Transfer Learning Method Combining Uncertainty with Diversity for Chinese Address Resolution 不确定性与多样性相结合的主动迁移学习方法在中文地址解析中的应用

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581902

Yuwei Hu, Xueyuan Zheng, Ping Zong

{"title":"An Active Transfer Learning Method Combining Uncertainty with Diversity for Chinese Address Resolution","authors":"Yuwei Hu, Xueyuan Zheng, Ping Zong","doi":"10.1145/3581807.3581902","DOIUrl":"https://doi.org/10.1145/3581807.3581902","url":null,"abstract":"Chinese address resolution (CAR) is a key step in geocoding technology, and the resolution results directly affect the service quality of address-based applications. Deep learning models have been widely used in CAR task but they require abundant annotated address data to obtain satisfied performance. In this paper, an active transfer learning method combining uncertainty with diversity for CAR is proposed, for which the main goal is to mitigate the annotation requirement for unlabeled address in the target region and to Improve the utilization of labeled data in the source region. Considering the correlation among Chinese addresses, we propose a clustering method of unlabeled address on the basis of feature words, mined from address data based on LDA model, to reflect the distribution of the address. A metric of comprehensive sample strategy combing uncertainty with diversity (CSSCUD) is constructed to select training samples from the target region, which can obtain high valuable samples by considering informativeness and distribution in feature words space jointly in each batch. Experiments on the address dataset from two different regions show that the comprehensive active transfer learning method achieves a higher resolution accuracy than various baselines by using the same number of labeled training samples, which illustrates that the proposed method is effective and practical for CAR.","PeriodicalId":292813,"journal":{"name":"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134511443","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