作者机构：
[Liu, Ming; E, Jinxuan; Liu, M; He, Chao] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.;[Liu, Rong] Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan, Peoples R China.
会议名称：
5th International Conference on Computing, Networks and Internet of Things (CNIOT)
会议时间：
MAY 24-26, 2024
会议地点：
Tokyo, JAPAN
会议主办单位：
[He, Chao;E, Jinxuan;Liu, Ming] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.^[Liu, Rong] Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan, Peoples R China.
摘要：
Virtual try-on is an image generation task for changing characters' clothes while preserving the characters' and the cloth's original attributes. Existing methods usually apply the traditional appearance flow method, which is susceptible to complex body postures or occlusions, leading to unclear texture of target clothing or distorted limbs of characters. We apply a StyleGAN-based(generative adversarial network) flow generator to estimate appearance flow, which provides more global information to overcome the issue. Additionally, more local information is captured to refine the appearance flow by adopting the channel attention mechanism. Qualitative and quantitative experiments demonstrate that our model can generate more realistic images.
期刊：
Journal of Physics: Conference Series,2023年2522(1):012023 ISSN：1742-6588
作者机构：
[Yu Wang; Liu Ming] School of Computer Science, Central China Normal University, Wuhan, China
摘要：
<jats:title>Abstract</jats:title>
<jats:p>Link prediction is a basic method to study complex networks, which has important practical application value. This paper proposes a global path link prediction method based on resource allocation for complex network topology information. Firstly, we improve the current quantization method of node resource transmission capability, and then consider the global path information in the network structure comprehensively. Finally, the link prediction method is defined based on the quantized resource transmission capability and global path information. Compared with 11 classical similarity algorithms, the experimental results show that the link prediction accuracy of this method has been improved to some extent, among which the highest improvement is 22.85%.</jats:p>
期刊：
Information Processing & Management,2023年60(2):103207 ISSN：0306-4573
通讯作者：
Chen Qiu
作者机构：
[Gu, Jinguang; Qiu, Chen; Xu, Zhaoyang; Liu, Maofu; Fu, Haidong] Wuhan Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430081, Peoples R China.;[Gu, Jinguang; Qiu, Chen; Xu, Zhaoyang; Liu, Maofu; Fu, Haidong] Wuhan Univ Sci & Technol, Inst Big Data Sci & Engn, Wuhan 430081, Peoples R China.;[Zhou, Guangyou] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Chen Qiu] S;School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430081, China<&wdkj&>Institute of Big Data Science and Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China
期刊：
IEEE Open Journal of the Computer Society,2021年2:393-406 ISSN：2644-1268
作者机构：
[Fei Ge; Wei Zhang; Ming Liu] Computer Science Department, Central China Normal University, Wuhan, Hubei, P. R. China;Discipline of ICT, School of Technology, Environments and Design, University of Tasmania, Hobart, TAS, Australia;Computer Science Department, Huazhong Normal University, Wuhan, Hubei, P. R. China;[Xun Gao] Electronic Engineering Department, Wuhan University, Wuhan, Hubei, P. R. China;[Juan Luo] College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, P. R. China
作者：
Xinhui Tu;Jing Luo;Bo Li;Tingting He;Maofu Liu
期刊：
International Conference on Information and Knowledge Management, Proceedings,2013年:1237-1240
作者机构：
[Jing Luo; Xinhui Tu; Maofu Liu] College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China;[Bo Li; Tingting He] Department of Computer Science, Central China Normal University, Wuhan, China;[Jing Luo; Xinhui Tu; Maofu Liu] Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, China
会议名称：
The 33rd ACM International Conference on Information and Knowledge Management
会议时间：
October 21 - 25, 2024
会议地点：
Boise , ID , USA
会议论文集名称：
CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management
摘要：
A main challenge in applying translation language models to information retrieval is how to estimate the "true" probability that a query could be generated as a translation of a document. The state-of-art methods rely on document-based word co-occurrences to estimate word-word translation probabilities. However, these methods do not take into account the proximity of co-occurrences. Intuitively, the proximity of co-occurrences can be exploited to estimate more accurate translation probabilities, since two words occur closer are more likely to be related. In this paper, we study how to explicitly incorporate proximity information into the existing translation language model, and propose a proximity-based translation language model, called TM-P, with three variants. In our TM-P models, a new concept (proximity-based word co-occurrence frequency) is introduced to model the proximity of word co-occurrences, which is then used to estimate translation probabilities. Experimental results on standard TREC collections show that our TM-P models achieve significant improvements over the state-of-the-art translation models. Copyright 2013 ACM.
