Fengmao Lv

1.7k total citations
58 papers, 1.0k citations indexed

About

Fengmao Lv is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Fengmao Lv has authored 58 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Artificial Intelligence, 21 papers in Computer Vision and Pattern Recognition and 8 papers in Signal Processing. Recurrent topics in Fengmao Lv's work include Domain Adaptation and Few-Shot Learning (13 papers), Multimodal Machine Learning Applications (8 papers) and Advanced Neural Network Applications (5 papers). Fengmao Lv is often cited by papers focused on Domain Adaptation and Few-Shot Learning (13 papers), Multimodal Machine Learning Applications (8 papers) and Advanced Neural Network Applications (5 papers). Fengmao Lv collaborates with scholars based in China, Singapore and United States. Fengmao Lv's co-authors include Lixin Duan, Guosheng Lin, Qing Lian, Boqing Gong, Guowu Yang, Yanyong Huang, Lei Feng, Wenyong Wang, Xiang Chen and Meng Wang and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Geophysical Research Letters and Environmental Pollution.

In The Last Decade

Fengmao Lv

51 papers receiving 1.0k citations

Peers

Fengmao Lv
Changsheng Li China
Ali Thabet Saudi Arabia
Manosij Ghosh India
Xiaoshan Yang China
Yunhui Guo United States
Manar Ahmed Hamza Saudi Arabia
Yirui Wu China
Haoxuan You United States
Quan Cui China
Boonserm Kijsirikul Thailand
Changsheng Li China
Fengmao Lv
Citations per year, relative to Fengmao Lv Fengmao Lv (= 1×) peers Changsheng Li

Countries citing papers authored by Fengmao Lv

Since Specialization
Citations

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

Fields of papers citing papers by Fengmao Lv

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fengmao Lv

This figure shows the co-authorship network connecting the top 25 collaborators of Fengmao Lv. A scholar is included among the top collaborators of Fengmao Lv 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 Fengmao Lv. Fengmao Lv 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.
Liu, Peng, Jinhong Deng, Lixin Duan, Wen Li, & Fengmao Lv. (2025). Segmenting Anything in the Dark via Depth Perception. IEEE Transactions on Multimedia. 27. 2975–2986.
2.
3.
Li, Tianrui, et al.. (2025). Adaptive Multi-Scale Language Reinforcement for Multimodal Named Entity Recognition. IEEE Transactions on Multimedia. 27. 5312–5323. 4 indexed citations
4.
He, Jin, Fengmao Lv, Jun Liu, et al.. (2025). C2T-HR3D: Cross-Fusion of CNN and Transformer for High-Speed Railway Dropper Defect Detection. IEEE Transactions on Instrumentation and Measurement. 74. 1–16. 2 indexed citations
5.
Liu, Xinyao, et al.. (2025). A pre-trained data deduplication model based on active learning. Expert Systems with Applications. 292. 128628–128628.
6.
He, Jin, et al.. (2025). Multi-Scale CNN-Transformer Hybrid Network for Rail Fastener Defect Detection. IEEE Transactions on Intelligent Transportation Systems. 26(6). 8894–8906. 6 indexed citations
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Zeng, Yijie, et al.. (2024). VLAI: Exploration and Exploitation based on Visual-Language Aligned Information for Robotic Object Goal Navigation. Image and Vision Computing. 151. 105259–105259. 4 indexed citations
9.
Wu, Liang, et al.. (2024). Source-free domain adaptation with unrestricted source hypothesis. Pattern Recognition. 149. 110246–110246. 9 indexed citations
10.
Lv, Fengmao, Chun Nie, Jianyang Zhang, et al.. (2024). Rethinking the Effect of Uninformative Class Name in Prompt Learning. 8345–8354. 1 indexed citations
11.
Wu, Zhenyu, Wei Wang, Lin Wang, et al.. (2024). Pixel is All You Need: Adversarial Spatio-Temporal Ensemble Active Learning for Salient Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(2). 858–877. 4 indexed citations
12.
Lyu, Yan, et al.. (2024). Co‐Occurring Extremes of Fine Particulate Matter (PM2.5) and Ground‐Level Ozone in the Summer of Southern China. Geophysical Research Letters. 51(2). 7 indexed citations
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Huang, Yanyong, Yuxin Cai, Xiuwen Yi, et al.. (2023). C2IMUFS: Complementary and Consensus Learning-Based Incomplete Multi-View Unsupervised Feature Selection. IEEE Transactions on Knowledge and Data Engineering. 35(10). 10681–10694. 24 indexed citations
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Huang, Yanyong, et al.. (2021). Adaptive graph-based generalized regression model for unsupervised feature selection. Knowledge-Based Systems. 227. 107156–107156. 29 indexed citations
18.
Lv, Fengmao, Liang Tao, Xiang Chen, & Guosheng Lin. (2020). Cross-Domain Semantic Segmentation via Domain-Invariant Interactive Relation Transfer. 4333–4342. 57 indexed citations
19.
Feng, Lei, et al.. (2020). Can Cross Entropy Loss Be Robust to Label Noise?. 2206–2212. 76 indexed citations
20.
Lv, Fengmao, et al.. (2017). The convergence and termination criterion of quantum-inspired evolutionary neural networks. Neurocomputing. 238. 157–167. 12 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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