2020 25th International Conference on Pattern Recognition (ICPR)最新文献_第3页

Auto Encoding Explanatory Examples with Stochastic Paths 随机路径的自动编码解释性示例

2020 25th International Conference on Pattern Recognition (ICPR) Pub Date : 2021-01-10 DOI: 10.1109/ICPR48806.2021.9413267

C. Ojeda, Ramsés J. Sánchez, K. Cvejoski, J. Schücker, C. Bauckhage, B. Georgiev

引用次数: 0

Activity Recognition Using First-Person-View Cameras Based on Sparse Optical Flows 基于稀疏光流的第一人称视角相机活动识别

2020 25th International Conference on Pattern Recognition (ICPR) Pub Date : 2021-01-10 DOI: 10.1109/ICPR48806.2021.9412330

Peng Yua Kao, Yan-Jing Lei, Chia-Hao Chang, Chu-Song Chen, Ming-Sui Lee, Y. Hung

引用次数: 2

GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Semantic Segmentation 语义分割中多尺度特征学习的门控尺度转移操作

2020 25th International Conference on Pattern Recognition (ICPR) Pub Date : 2021-01-10 DOI: 10.1109/ICPR48806.2021.9412965

Zhuoying Wang, Yongtao Wang, Zhi Tang, Yangyan Li, Ying Chen, Haibin Ling, Weisi Lin

{"title":"GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Semantic Segmentation","authors":"Zhuoying Wang, Yongtao Wang, Zhi Tang, Yangyan Li, Ying Chen, Haibin Ling, Weisi Lin","doi":"10.1109/ICPR48806.2021.9412965","DOIUrl":"https://doi.org/10.1109/ICPR48806.2021.9412965","url":null,"abstract":"Existing CNN-based methods for semantic segmentation heavily depend on multi-scale features to meet the requirements of both semantic comprehension and detail preservation. State-of-the-art segmentation networks widely exploit conventional scale-transfer operations, i.e., up-sampling and down-sampling to learn multi-scale features. In this work, we find that these operations lead to scale-confused features and suboptimal performance because they are spatial-invariant and directly transit all feature information cross scales without spatial selection. To address this issue, we propose the Gated Scale-Transfer Operation (GSTO) to properly transit spatial-filtered features to another scale. Specifically, GSTO can work either with or without extra supervision. Unsupervised GSTO is learned from the feature itself while the supervised one is guided by the supervised probability matrix. Both forms of GSTO are lightweight and plug-and-play, which can be flexibly integrated into networks or modules for learning better multi-scale features. In particular, by plugging GSTO into HRNet, we get a more powerful backbone (namely GSTO-HRNet) for pixel labeling, and it achieves new state-of-the-art results on multiple benchmarks for semantic segmentation including Cityscapes, LIP, and Pascal Context, with a negligible extra computational cost. Moreover, experiment results demonstrate that GSTO can also significantly boost the performance of multi-scale feature aggregation modules like PPM and ASPP.","PeriodicalId":6783,"journal":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","volume":"5 1","pages":"7111-7118"},"PeriodicalIF":0.0,"publicationDate":"2021-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79138369","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: 2

Automatic Detection of Stationary Waves in the Venus Atmosphere Using Deep Generative Models 利用深度生成模型自动探测金星大气中的驻波

2020 25th International Conference on Pattern Recognition (ICPR) Pub Date : 2021-01-10 DOI: 10.1109/ICPR48806.2021.9413038

Minori Narita, Daiki Kimura, T. Imamura

引用次数: 0

Generic Merging of Structure from Motion Maps with a Low Memory Footprint 低内存占用运动地图结构的通用合并

2020 25th International Conference on Pattern Recognition (ICPR) Pub Date : 2021-01-10 DOI: 10.1109/ICPR48806.2021.9412879

Gabrielle Flood, David Gillsjö, Patrik Persson, A. Heyden, K. Åström

{"title":"Generic Merging of Structure from Motion Maps with a Low Memory Footprint","authors":"Gabrielle Flood, David Gillsjö, Patrik Persson, A. Heyden, K. Åström","doi":"10.1109/ICPR48806.2021.9412879","DOIUrl":"https://doi.org/10.1109/ICPR48806.2021.9412879","url":null,"abstract":"With the development of cheap image sensors, the amount of available image data have increased enormously, and the possibility of using crowdsourced collection methods has emerged. This calls for development of ways to handle all these data. In this paper, we present new tools that will enable efficient, flexible and robust map merging. Assuming that separate optimisations have been performed for the individual maps, we show how only relevant data can be stored in a low memory footprint representation. We use these representations to perform map merging so that the algorithm is invariant to the merging order and independent of the choice of coordinate system. The result is a robust algorithm that can be applied to several maps simultaneously. The result of a merge can also be represented with the same type of low-memory footprint format, which enables further merging and updating of the map in a hierarchical way. Furthermore, the method can perform loop closing and also detect changes in the scene between the capture of the different image sequences. Using both simulated and real data — from both a hand held mobile phone and from a drone — we verify the performance of the proposed method.","PeriodicalId":6783,"journal":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","volume":"42 1","pages":"4385-4392"},"PeriodicalIF":0.0,"publicationDate":"2021-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81272329","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

XGBoost to Interpret the Opioid Patients' State Based on Cognitive and Physiological Measures 基于认知和生理测量的XGBoost解释阿片类药物患者的状态

2020 25th International Conference on Pattern Recognition (ICPR) Pub Date : 2021-01-10 DOI: 10.1109/ICPR48806.2021.9412464

O. Dehzangi, Arash Shokouhmand, P. Jeihouni, J. Ramadan, V. Finomore, N. Nasrabadi, A. Rezai

{"title":"XGBoost to Interpret the Opioid Patients' State Based on Cognitive and Physiological Measures","authors":"O. Dehzangi, Arash Shokouhmand, P. Jeihouni, J. Ramadan, V. Finomore, N. Nasrabadi, A. Rezai","doi":"10.1109/ICPR48806.2021.9412464","DOIUrl":"https://doi.org/10.1109/ICPR48806.2021.9412464","url":null,"abstract":"Dealing with opioid addiction and its long-term consequences is of great importance, as the addiction to opioids is emerged gradually, and established strongly in a given patient's body. Based on recent research, quitting the opioid requires clinicians to arrange a gradual plan for the patients who deal with the difficulties of overcoming addiction. This, in turn, necessitates observing the patients' wellness periodically, which is conventionally made by setting clinical appointments. With the advent of wearable sensors continuous patient monitoring becomes possible. However, the data collected through the sensors is pervasively noisy, where using sensors with different sampling frequency challenges the data processing. In this work, we handle this problem by using data from cognitive tests, along with heart rate (HR) and heart rate variability (HRV). The proposed recipe enables us to interpret the data as a feature space, where we can predict the wellness of the opioid patients by employing extreme gradient boosting (XGBoost), which results in 96.12% average accuracy of prediction as the best achieved performance.","PeriodicalId":6783,"journal":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","volume":"51 1","pages":"6391-6395"},"PeriodicalIF":0.0,"publicationDate":"2021-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81287181","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

VSB2-Net: Visual-Semantic Bi-Branch Network for Zero-Shot Hashing VSB2-Net:零次哈希的视觉语义双分支网络

2020 25th International Conference on Pattern Recognition (ICPR) Pub Date : 2021-01-10 DOI: 10.1109/ICPR48806.2021.9412798

Xin Li, Xiangfeng Wang, Bo Jin, Wenjie Zhang, Jun Wang, H. Zha

{"title":"VSB2-Net: Visual-Semantic Bi-Branch Network for Zero-Shot Hashing","authors":"Xin Li, Xiangfeng Wang, Bo Jin, Wenjie Zhang, Jun Wang, H. Zha","doi":"10.1109/ICPR48806.2021.9412798","DOIUrl":"https://doi.org/10.1109/ICPR48806.2021.9412798","url":null,"abstract":"Zero-shot hashing aims at learning hashing model from seen classes and the obtained model is capable of generalizing to unseen classes for image retrieval. Inspired by zero-shot learning, existing zero-shot hashing methods usually transfer the supervised knowledge from seen to unseen classes, by embedding the hamming space to a shared semantic space. However, this makes instances difficult to distinguish due to limited hashing bit numbers, especially for semantically similar unseen classes. We propose a novel inductive zero-shot hashing framework, i.e., VSB2-Net, where both semantic space and visual feature space are embedded to the same hamming space instead. The reconstructive semantic relationships are established in the hamming space, preserving local similarity relationships and explicitly enlarging the discrepancy between semantic hamming vectors. A two-task architecture, comprising of classification module and visual feature reconstruction module, is employed to enhance the generalization and transfer abilities. Extensive evaluation results on several benchmark datasets demonstrate the superiority of our proposed method compared to several state-of-the-art baselines.","PeriodicalId":6783,"journal":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","volume":"18 1","pages":"1836-1843"},"PeriodicalIF":0.0,"publicationDate":"2021-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81609057","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

Cut and Compare: End-to-end Offline Signature Verification Network Cut and Compare:端到端离线签名验证网络

2020 25th International Conference on Pattern Recognition (ICPR) Pub Date : 2021-01-10 DOI: 10.1109/ICPR48806.2021.9412377

Xinyi Lu, Linlin Huang, Fei Yin

引用次数: 4

Object Detection on Monocular Images with Two- Dimensional Canonical Correlation Analysis 基于二维典型相关分析的单眼图像目标检测

2020 25th International Conference on Pattern Recognition (ICPR) Pub Date : 2021-01-10 DOI: 10.1109/ICPR48806.2021.9412067

Zifan Yu, Suya You

引用次数: 0

Disentangled Representation Learning for Controllable Image Synthesis: An Information-Theoretic Perspective 基于信息论视角的可控图像合成的解纠缠表示学习

2020 25th International Conference on Pattern Recognition (ICPR) Pub Date : 2021-01-10 DOI: 10.1109/ICPR48806.2021.9411925

Shichang Tang, Xueying Zhou, Xuming He, Yi Ma

引用次数: 3