Jian-Hao Luo

3.4k total citations · 1 hit paper
9 papers, 1.8k citations indexed

About

Jian-Hao Luo is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Jian-Hao Luo has authored 9 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computer Vision and Pattern Recognition, 4 papers in Artificial Intelligence and 1 paper in Computational Mechanics. Recurrent topics in Jian-Hao Luo's work include Advanced Neural Network Applications (5 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Image Retrieval and Classification Techniques (2 papers). Jian-Hao Luo is often cited by papers focused on Advanced Neural Network Applications (5 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Image Retrieval and Classification Techniques (2 papers). Jian-Hao Luo collaborates with scholars based in China and United Kingdom. Jian-Hao Luo's co-authors include Jianxin Wu, Weiyao Lin, Xiu-Shen Wei, Zhi‐Hua Zhou, Chen-Wei Xie, Hong-Yu Zhou, Guosheng Hu, Zhenhua Feng, Dong‐Jun Yu and Xiaoning Song and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Pattern Recognition.

In The Last Decade

Jian-Hao Luo

9 papers receiving 1.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression

2017 1.1k citations Jian-Hao Luo, Jianxin Wu et al. profile →

Peers

Jian-Hao Luo

CVPR

AI

EEE

MT

CNC

Péter Vajda United States

Peizhao Zhang United States

Yuandong Tian United States

Mingbao Lin China

Arun Mallya United States

Marc Masana Austria

Bohan Zhuang Australia

Hakan Bilen United Kingdom

Jianwei Yang United States

Yongjian Wu China

Péter Vajda United States

Jian-Hao Luo

1.2k ×0.9

CVPR

857 ×0.8

AI

206 ×1.5

EEE

110 ×1.2

MT

74 ×0.9

CNC

Citations per year, relative to Jian-Hao Luo Jian-Hao Luo (= 1×) peers Péter Vajda

Countries citing papers authored by Jian-Hao Luo

Since Specialization

Citations

This map shows the geographic impact of Jian-Hao Luo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jian-Hao Luo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jian-Hao Luo more than expected).

Fields of papers citing papers by Jian-Hao Luo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jian-Hao Luo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jian-Hao Luo. The network helps show where Jian-Hao Luo may publish in the future.

Co-authorship network of co-authors of Jian-Hao Luo

This figure shows the co-authorship network connecting the top 25 collaborators of Jian-Hao Luo. A scholar is included among the top collaborators of Jian-Hao Luo based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jian-Hao Luo. Jian-Hao Luo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Luo, Jian-Hao, et al.. (2025). Research on High-Precision Gas Concentration Inversion for Imaging Fourier Transform Spectroscopy Based on Multi-Scale Feature Attention Model. Applied Sciences. 15(5). 2438–2438. 1 indexed citations

2.

Luo, Jian-Hao & Jianxin Wu. (2020). AutoPruner: An end-to-end trainable filter pruning method for efficient deep model inference. Pattern Recognition. 107. 107461–107461. 126 indexed citations

3.

Luo, Jian-Hao & Jianxin Wu. (2020). Neural Network Pruning With Residual-Connections and Limited-Data. 1455–1464. 83 indexed citations

4.

Song, Xiaoning, Guosheng Hu, Jian-Hao Luo, et al.. (2019). Fast SRC using quadratic optimisation in downsized coefficient solution subspace. Signal Processing. 161. 101–110. 5 indexed citations

5.

Luo, Jian-Hao, et al.. (2018). ThiNet: Pruning CNN Filters for a Thinner Net. IEEE Transactions on Pattern Analysis and Machine Intelligence. 41(10). 2525–2538. 167 indexed citations

6.

Wu, Jianxin, Bin-Bin Gao, Xiu-Shen Wei, & Jian-Hao Luo. (2018). Resource-constrained deep learning: challenges and practices. Scientia Sinica Informationis. 48(5). 501–510. 4 indexed citations

7.

Luo, Jian-Hao, Jianxin Wu, & Weiyao Lin. (2017). ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression. 5068–5076. 1086 indexed citations breakdown →

8.

Wei, Xiu-Shen, Jian-Hao Luo, Jianxin Wu, & Zhi‐Hua Zhou. (2017). Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval. IEEE Transactions on Image Processing. 26(6). 2868–2881. 318 indexed citations

9.

Luo, Jian-Hao, Zhou Wang, & Jianxin Wu. (2016). Image categorization with resource constraints: introduction, challenges and advances. Frontiers of Computer Science. 11(1). 13–26. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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