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106 results on '"Yu, Zhezhou"'

3. Analysis of Public Transport System Efficiency Based on Multi-layer Network

Author

Xu, Lu, Yang, Funing, Yu, Zhezhou, Huang, Qiuyang, Ji, Xuan, Xu, Yuanbo, 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, Sun, Xingming, editor, and Wang, Jinwei, editor

Published
2020
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4. Image Captioning Based on Semantic Scenes.

Author

Zhao, Fengzhi, Yu, Zhezhou, Wang, Tao, and Lv, Yi

Subjects
*NATURAL language processing, *COMPUTER vision, *ARTIFICIAL intelligence, *IMAGE retrieval, *ENCYCLOPEDIAS & dictionaries, *DEEP learning
Abstract

With the development of artificial intelligence and deep learning technologies, image captioning has become an important research direction at the intersection of computer vision and natural language processing. The purpose of image captioning is to generate corresponding natural language descriptions by understanding the content of images. This technology has broad application prospects in fields such as image retrieval, autonomous driving, and visual question answering. Currently, many researchers have proposed region-based image captioning methods. These methods generate captions by extracting features from different regions of an image. However, they often rely on local features of the image and overlook the understanding of the overall scene, leading to captions that lack coherence and accuracy when dealing with complex scenes. Additionally, image captioning methods are unable to extract complete semantic information from visual data, which may lead to captions with biases and deficiencies. Due to these reasons, existing methods struggle to generate comprehensive and accurate captions. To fill this gap, we propose the Semantic Scenes Encoder (SSE) for image captioning. It first extracts a scene graph from the image and integrates it into the encoding of the image information. Then, it extracts a semantic graph from the captions and preserves semantic information through a learnable attention mechanism, which we refer to as the dictionary. During the generation of captions, it combines the encoded information of the image and the learned semantic information to generate complete and accurate captions. To verify the effectiveness of the SSE, we tested the model on the MSCOCO dataset. The experimental results show that the SSE improves the overall quality of the captions. The improvement in scores across multiple evaluation metrics further demonstrates that the SSE possesses significant advantages when processing identical images. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Meshed Context-Aware Beam Search for Image Captioning.

Author

Zhao, Fengzhi, Yu, Zhezhou, Wang, Tao, and Zhao, He

Subjects
*LEXICAL access, *VOCABULARY, *ALGORITHMS
Abstract

Beam search is a commonly used algorithm in image captioning to improve the accuracy and robustness of generated captions by finding the optimal word sequence. However, it mainly focuses on the highest-scoring sequence at each step, often overlooking the broader image context, which can lead to suboptimal results. Additionally, beam search tends to select similar words across sequences, causing repetitive and less diverse output. These limitations suggest that, while effective, beam search can be further improved to better capture the richness and variety needed for high-quality captions. To address these issues, this paper presents meshed context-aware beam search (MCBS). In MCBS for image captioning, the generated caption context is dynamically used to influence the image attention mechanism at each decoding step, ensuring that the model focuses on different regions of the image to produce more coherent and contextually appropriate captions. Furthermore, a penalty coefficient is introduced to discourage the generation of repeated words. Through extensive testing and ablation studies across various models, our results show that MCBS significantly enhances overall model performance. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Grid Partition and Agglomeration for Bidirectional Hierarchical Clustering

Author

Wu, Lei, Chen, Hechang, Yu, Xiangchun, Chao, Sun, Yu, Zhezhou, Dou, RuiTing, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Xiaohua, Jia, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Li, Jin, editor, Liu, Zheli, editor, and Peng, Hao, editor

Published
2019
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14. Combining Deep Learning and Preference Learning for Object Tracking

Author

Pang, Shuchao, del Coz, Juan José, Yu, Zhezhou, Luaces, Oscar, Díez, Jorge, 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, Hirose, Akira, editor, Ozawa, Seiichi, editor, Doya, Kenji, editor, Ikeda, Kazushi, editor, Lee, Minho, editor, and Liu, Derong, editor

Published
2016
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18. Improved Gaussian Mixture Model for Moving Object Detection

Author

Chen, Gang, Yu, Zhezhou, Wen, Qing, Yu, Yangquan, 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, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Deng, Hepu, editor, Miao, Duoqian, editor, Lei, Jingsheng, editor, and Wang, Fu Lee, editor

Published
2011
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19. Dilated Convolutional Pixels Affinity Network for Weakly Supervised Semantic Segmentation

Author

YU Zhezhou, Zhang Zhe, LI Zhiyuan, and Wang Bilin

Subjects
Pixel, business.industry, Computer science, Applied Mathematics, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Pascal (programming language), Object (computer science), Convolutional neural network, True negative, Segmentation, Artificial intelligence, Electrical and Electronic Engineering, business, computer, computer.programming_language
Abstract

This paper studies semantic segmentation primarily under image-level weak-supervision. Most stateof-the-art technologies have recently used deep classification networks to create small and sparse discriminatory seed regions of each interest target as pseudo-labels for training segmentation networks, which achieve inferior performance compared with the fully supervised setting. We propose a Dilated convolutional pixels affinity network (DCPAN) to localize and expand the seed regions of objects to bridge this gap. Although introduced dilated convolutional units enable capture of additional location information of objects, it falsely highlighted true negative regions as dilated rate enlarge. To address this problem, we properly integrate dilated convolutional units with different dilated rates and self-attention mechanisms to obtain pixel affinity measure matrix for promoting classification network to generate high-quality object seed regions as pseudo-labels; thus, the performance of the segmentation network is boosted. Furthermore, although our approach seems simple, our method obtains a competitive performance, and experiments show that the performance of DCPAN outperforms other state-ofart approaches in weakly-supervised settings, which only use image-level labels on the Pascal VOC 2012 dataset.

Published
2021
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20. Design of Face Recognition Door Manager System Based on DSP

Author

Pu, Dongbing, Du, Changrui, Yu, Zhezhou, Zhou, Chunguang, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yang, Laurence T., editor, Amamiya, Makoto, editor, Liu, Zhen, editor, Guo, Minyi, editor, and Rammig, Franz J., editor

Published
2005
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21. Obscene Image Recognition Based on Model Matching and BWFNN

Author

Liu, Xiaohua, Yu, Zhezhou, Zhang, Libiao, Liu, Miao, Zhou, Chunguang, Li, Chunxia, Sun, Catitang, Zhang, Li, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, Jun, editor, Liao, Xiao-Feng, editor, and Yi, Zhang, editor

Published
2005
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22. A Fuzzy Neural Network System Based on Generalized Class Cover and Particle Swarm Optimization

Author

Huang, Yanxin, Wang, Yan, Zhou, Wengang, Yu, Zhezhou, Zhou, Chunguang, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Huang, De-Shuang, editor, Zhang, Xiao-Ping, editor, and Huang, Guang-Bin, editor

Published
2005
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