Search

Your search keyword '"Miao, Zhenjiang"' showing total 198 results
Start Over Author "Miao, Zhenjiang" Publication Year Range Last 10 years
198 results on '"Miao, Zhenjiang"'

3. A Brief Survey on Semantic-preserving Data Augmentation.

Author

Song, Shaoyue, Zhu, Qing, and Miao, Zhenjiang

Abstract

In the era of deep learning, it's important to obtain enough qualified data for model training. For the labor-intensive and time-consuming characteristics of data collection and annotation, date augmentation has been widely employed in deep learning methods to mitigate over-fitting. It has been proven to be one of the most effective ways to ensure the performance of deep models. In the field of computer vision, the principal goal of data augmentation usually is enlarging both the scale and diversity of the dataset, and maintaining the quality of both the image and the annotation data simultaneously. Data augmentation methods at the raw input data level are intuitive to obtain semantic meaningful images. In this paper, we provide a brief survey on some data augmentation studies conducted at image level and briefly categorize the studies into two main classes: those based on image editing and those based on image generation. We also make an analysis of the semantic-preserving considerations within data augmentation techniques. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

5. Aggregating Motion and Attention for Video Object Detection

Author

Zhang, Ruyi, Miao, Zhenjiang, Ma, Cong, Hao, Shanshan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Palaiahnakote, Shivakumara, editor, Sanniti di Baja, Gabriella, editor, Wang, Liang, editor, and Yan, Wei Qi, editor

Published
2020
Full Text
View/download PDF

6. Skeleton-Based Labanotation Generation Using Multi-model Aggregation

Author

Xie, Ningwei, Miao, Zhenjiang, Wang, Jiaji, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Palaiahnakote, Shivakumara, editor, Sanniti di Baja, Gabriella, editor, Wang, Liang, editor, and Yan, Wei Qi, editor

Published
2020
Full Text
View/download PDF

8. Global Anomaly Detection Based on a Deep Prediction Neural Network

Author

Li, Ang, Miao, Zhenjiang, Cen, Yigang, Mladenovic, Vladimir, Liang, Liequan, Zheng, Xinwei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Milošević, Danijela, editor, Tang, Yong, editor, and Zu, Qiaohong, editor

Published
2019
Full Text
View/download PDF

9. Multi-scale Feature and Spatial Relation Inference for Object Detection

Author

Zhou, Tianyu, Miao, Zhenjiang, Wang, Jiaji, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhao, Yao, editor, Barnes, Nick, editor, Chen, Baoquan, editor, Westermann, Rüdiger, editor, Kong, Xiangwei, editor, and Lin, Chunyu, editor

Published
2019
Full Text
View/download PDF

10. Detecting Anomalous Trajectories via Recurrent Neural Networks

Author

Ma, Cong, Miao, Zhenjiang, Li, Min, Song, Shaoyue, Yang, Ming-Hsuan, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Jawahar, C.V., editor, Li, Hongdong, editor, Mori, Greg, editor, and Schindler, Konrad, editor

Published
2019
Full Text
View/download PDF

14. Preparation and properties of BAS/SiC complex phase ceramic materials

Author

YANG Xiaobo, SUN Zhiqiang, ZHANG Bingqing, MIAO Zhenjiang, WANG Huadong, and LYU Yi

Subjects
celsian, silicon carbide, pressureless sintering, mechanical properties, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Abstract

Celsian was synthesized in situ and was used to sinter silicon carbide. The low temperature pressureless liquid sintering process for BAS/SiC complex phase ceramics was studied, and ceramic materials with high density were prepared. The effect of sintering temperature and content of BAS on density, structure and mechanical properties of the ceramics were investigated. The densification and microstructures of BAS/SiC ceramics were observed by scanning electron microscope. The crystalline phase of the composite ceramics was identified by X-ray powder diffraction. The results show that celsian is synthesized at the temperature of 1800 ℃, and BAS/SiC ceramics are prepared with density of 3.2 g/cm3. BAS is precipitated in hexagonal structure, and SiC particles are distributed uniformly in the composite ceramic. The flexural strength and fracture toughness of composite ceramics are decreased due to grains growth while the sintering temperature is above 1800 ℃. When the mass content of BAS reaches 30%, the typical flexural strength, elastic modulus and fracture toughness of the pressureless sintered BAS/SiC ceramics are 413 MPa, 210 GPa and 5.03 MPa?m1/2 at room temperature.

Published
2019
Full Text
View/download PDF

18. Research on Vehicle Type Classification Based on Spatial Pyramid Representation and BP Neural Network

Author

Song, Shaoyue, Miao, Zhenjiang, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, and Zhang, Yu-Jin, editor

Published
2015
Full Text
View/download PDF

19. Learning Spatio-Temporal Features for Action Recognition with Modified Hidden Conditional Random Field

Author

Xu, Wanru, Miao, Zhenjiang, Zhang, Jian, Tian, Yi, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Agapito, Lourdes, editor, Bronstein, Michael M., editor, and Rother, Carsten, editor

Published
2015
Full Text
View/download PDF

47. Prediction-CGAN

Author

Xu, Wanru, primary, Yu, Jian, additional, Miao, Zhenjiang, additional, Wan, Lili, additional, and Ji, Qiang, additional

Published
2019
Full Text
View/download PDF

49. Combining CNN with Hand-Crafted Features for Image Classification

Author

Zhou Tianyu, Zhang Jianhu, and Miao Zhenjiang

Subjects
Contextual image classification, business.industry, Computer science, Feature extraction, Pattern recognition, 02 engineering and technology, 021001 nanoscience & nanotechnology, Convolutional neural network, Feature (computer vision), Histogram, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Enhanced Data Rates for GSM Evolution, Artificial intelligence, 0210 nano-technology, business, Encoder
Abstract

Convolutional neural networks (CNN) have achieved outstanding performance in image recognition tasks, but many hand-crafted features still play important roles in some areas. Hand-crafted features are designed to describe image content from specific aspects, which may provide complementary information for CNN in image classification tasks. This paper explores feature fusion methods and proposes a novel framework for combining CNN with hand-crafted features. The framework has two main advantages. First, feature encoder can encode non-normalized features in CNN, which takes advantage of some good edge, texture and local features. Second, joint training strategy makes features fuse better in CNN. We validate that many handcrafted features help to improve the performance of origin CNN. Experiments show our method outperforms the origin CaffeNet on Cifar10 dataset with 79.16% accuracy.

Published
2018

Catalog

Books, media, physical & digital resources
See catalog results