Qirong Mao

4.0k total citations · 1 hit paper
120 papers, 2.7k citations indexed

About

Qirong Mao is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Qirong Mao has authored 120 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 63 papers in Computer Vision and Pattern Recognition, 60 papers in Artificial Intelligence and 48 papers in Signal Processing. Recurrent topics in Qirong Mao's work include Emotion and Mood Recognition (39 papers), Speech and Audio Processing (38 papers) and Face and Expression Recognition (34 papers). Qirong Mao is often cited by papers focused on Emotion and Mood Recognition (39 papers), Speech and Audio Processing (38 papers) and Face and Expression Recognition (34 papers). Qirong Mao collaborates with scholars based in China, United States and Ghana. Qirong Mao's co-authors include Yongzhao Zhan, Ming Dong, Zheng-Wei Huang, Feifei Zhang, Changsheng Xu, Tianzhu Zhang, Jingjing Chen, Jianping Gou, Ling Zhou and Hongjie Jia and has published in prestigious journals such as IEEE Transactions on Image Processing, Expert Systems with Applications and IEEE Access.

In The Last Decade

Qirong Mao

114 papers receiving 2.6k citations

Hit Papers

Learning Salient Features for Speech Emotion <newline/... 2014 2026 2018 2022 2014 100 200 300 400
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.

  1. Learning Salient Features for Speech Emotion <newline/>Recognition Using Convolutional <newline/>Neural Networks
    2014 412 citations Qirong Mao, Zheng-Wei Huang et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Qirong Mao China 27 1.3k 1.2k 1.1k 988 194 120 2.7k
Irene Kotsia United Kingdom 20 1.4k 1.1× 2.5k 2.0× 567 0.5× 834 0.8× 293 1.5× 43 3.5k
George Trigeorgis United Kingdom 13 747 0.6× 970 0.8× 624 0.6× 769 0.8× 158 0.8× 17 1.9k
Juergen Luettin Switzerland 22 897 0.7× 2.5k 2.1× 735 0.7× 1.9k 1.9× 269 1.4× 46 3.8k
Xiaojiang Peng China 23 1.1k 0.9× 2.3k 1.9× 874 0.8× 213 0.2× 136 0.7× 81 2.9k
Tomas Pfister United States 17 740 0.6× 1.7k 1.4× 1.3k 1.2× 231 0.2× 151 0.8× 52 3.0k
John See Malaysia 27 1.0k 0.8× 1.7k 1.4× 612 0.6× 264 0.3× 221 1.1× 121 2.4k
Peng Song China 22 1.1k 0.8× 547 0.4× 716 0.7× 472 0.5× 881 4.5× 108 2.4k
Mihalis A. Nicolaou United Kingdom 21 1.5k 1.2× 1.1k 0.9× 792 0.8× 922 0.9× 352 1.8× 50 2.6k
Yan Tong United States 18 1.0k 0.8× 1.3k 1.1× 299 0.3× 213 0.2× 160 0.8× 84 1.9k
Jianwu Dang China 27 982 0.8× 323 0.3× 1.7k 1.6× 1.3k 1.4× 342 1.8× 347 3.0k

Countries citing papers authored by Qirong Mao

Since Specialization
Citations

This map shows the geographic impact of Qirong Mao'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 Qirong Mao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qirong Mao more than expected).

Fields of papers citing papers by Qirong Mao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Qirong Mao. 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 Qirong Mao. The network helps show where Qirong Mao may publish in the future.

Co-authorship network of co-authors of Qirong Mao

This figure shows the co-authorship network connecting the top 25 collaborators of Qirong Mao. A scholar is included among the top collaborators of Qirong Mao 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 Qirong Mao. Qirong Mao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Mao, Qirong, et al.. (2025). Towards a Robust Group-Level Emotion Recognition via Uncertainty-Aware Learning. IEEE Transactions on Affective Computing. 16(3). 1970–1983. 1 indexed citations
3.
Mao, Qirong, et al.. (2025). Hierarchical Temporal Sequence Segmentation for weakly supervised video anomaly detection. Expert Systems with Applications. 295. 128753–128753.
4.
Chang, Qinglin, et al.. (2025). A Comprehensive Review of Digital Twins Technology in Agriculture. Agriculture. 15(9). 903–903. 14 indexed citations
5.
Jiang, Zhen, et al.. (2024). A post-processing framework for class-imbalanced learning in a transductive setting. Expert Systems with Applications. 249. 123832–123832. 5 indexed citations
6.
Mao, Qirong, et al.. (2023). TE-KWS: Text-Informed Speech Enhancement for Noise-Robust Keyword Spotting. 601–610. 1 indexed citations
7.
Mao, Qirong, et al.. (2023). A novel conversational hierarchical attention network for speech emotion recognition in dyadic conversation. Multimedia Tools and Applications. 83(21). 59699–59723. 6 indexed citations
8.
Jiang, Zhen, et al.. (2023). A semi-supervised resampling method for class-imbalanced learning. Expert Systems with Applications. 221. 119733–119733. 33 indexed citations
9.
Ma, Lizhuang, et al.. (2022). Visual recognition technologies for complex scenarios analysis. Journal of Image and Graphics. 27(6). 1723–1742. 1 indexed citations
10.
Ren, Qinghua, et al.. (2022). Multi-branch feature aggregation based on multiple weighting for speaker verification. Computer Speech & Language. 77. 101426–101426. 3 indexed citations
11.
Liu, Honglin, Yongzhao Zhan, Huifen Xia, Qirong Mao, & Yixin Tan. (2022). Self-supervised transformer-based pre-training method using latent semantic masking auto-encoder for pest and disease classification. Computers and Electronics in Agriculture. 203. 107448–107448. 33 indexed citations
12.
Mao, Qirong, et al.. (2021). Latent discriminative representation learning for speaker recognition. Frontiers of Information Technology & Electronic Engineering. 22(5). 697–708. 1 indexed citations
13.
Chen, Jingjing, et al.. (2020). Dual-Path Transformer Network: Direct Context-Aware Modeling for End-to-End Monaural Speech Separation. 2642–2646. 201 indexed citations
14.
Mao, Qirong, et al.. (2020). NLWSNet: a weakly supervised network for visual sentiment analysis in mislabeled web images. Frontiers of Information Technology & Electronic Engineering. 21(9). 1321–1333. 10 indexed citations
15.
16.
Gou, Jianping, et al.. (2018). A New Discriminative Collaborative Neighbor Representation Method for Robust Face Recognition. IEEE Access. 6. 74713–74727. 20 indexed citations
17.
Huang, Zheng-Wei, et al.. (2015). Speech emotion recognition with unsupervised feature learning. Frontiers of Information Technology & Electronic Engineering. 16(5). 358–366. 28 indexed citations
18.
Mao, Qirong, et al.. (2013). Speaker-independent speech emotion recognition by fusion of functional and accompanying paralanguage features. Journal of Zhejiang University SCIENCE C. 14(7). 573–582. 3 indexed citations
19.
20.
Zhou, Yuan, et al.. (2004). DRMR: Dynamic-ring-based multicast routing protocol for Ad hoc networks. Journal of Computer Science and Technology. 19(6). 909–919. 3 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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