Nan Pu

713 total citations
33 papers, 464 citations indexed

About

Nan Pu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Urban Studies. According to data from OpenAlex, Nan Pu has authored 33 papers receiving a total of 464 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Computer Vision and Pattern Recognition, 19 papers in Artificial Intelligence and 2 papers in Urban Studies. Recurrent topics in Nan Pu's work include Domain Adaptation and Few-Shot Learning (16 papers), Multimodal Machine Learning Applications (13 papers) and Video Surveillance and Tracking Methods (10 papers). Nan Pu is often cited by papers focused on Domain Adaptation and Few-Shot Learning (16 papers), Multimodal Machine Learning Applications (13 papers) and Video Surveillance and Tracking Methods (10 papers). Nan Pu collaborates with scholars based in China, Netherlands and Italy. Nan Pu's co-authors include Michael S. Lew, Erwin M. Bakker, Yu Liu, Nicu Sebe, Zhun Zhong, Yalan Qin, Hanzhou Wu, Wei Chen, Yu Liu and Wei Chen and has published in prestigious journals such as SHILAP Revista de lepidopterología, Environmental Science & Technology and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Nan Pu

27 papers receiving 461 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Nan Pu China	14	340	175	50	22	21	33	464
Rajeshreddy Datla India	8	164 0.5×	73 0.4×	35 0.7×	14 0.6×	69 3.3×	19	327
Alejandro H. Ballado Philippines	10	127 0.4×	60 0.3×	24 0.5×	26 1.2×	31 1.5×	61	341
Riccardo La Grassa Italy	9	103 0.3×	49 0.3×	21 0.4×	12 0.5×	41 2.0×	23	323
Cyril Carincotte France	7	141 0.4×	107 0.6×	11 0.2×	12 0.5×	91 4.3×	15	260
Manish Khare India	14	314 0.9×	65 0.4×	49 1.0×	5 0.2×	120 5.7×	50	465
Weiwei Li China	13	171 0.5×	154 0.9×	28 0.6×	9 0.4×	27 1.3×	44	448
Yi Kong China	10	104 0.3×	122 0.7×	33 0.7×	9 0.4×	180 8.6×	29	375
Ruiqi Wang China	11	103 0.3×	114 0.7×	18 0.4×	5 0.2×	8 0.4×	36	305
Tingzhao Yu China	11	108 0.3×	66 0.4×	18 0.4×	42 1.9×	22 1.0×	22	236
Yanmin Luo China	11	244 0.7×	85 0.5×	46 0.9×	37 1.7×	34 1.6×	39	369

Countries citing papers authored by Nan Pu

Since Specialization

Citations

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

Fields of papers citing papers by Nan Pu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nan Pu

This figure shows the co-authorship network connecting the top 25 collaborators of Nan Pu. A scholar is included among the top collaborators of Nan Pu 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 Nan Pu. Nan Pu 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:

20 of 20 papers shown

Pu, Nan, et al.. (2025). Few-shot event-based action recognition. Neural Networks. 191. 107750–107750.

Qin, Yalan, Nan Pu, Nicu Sebe, & Guorui Feng. (2025). Latent Space Learning-Based Ensemble Clustering. IEEE Transactions on Image Processing. 34. 1259–1270. 3 indexed citations

Zang, Qi, et al.. (2025). SeCoV2: Semantic Connectivity-Driven Pseudo-Labeling for Robust Cross-Domain Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(11). 10378–10395.

Jia, Qi, et al.. (2024). Bilevel progressive homography estimation via correlative region-focused transformer. Computer Vision and Image Understanding. 250. 104209–104209. 1 indexed citations

Pu, Nan, et al.. (2024). Fire and Smoke Detection with Burning Intensity Representation. 1–8. 2 indexed citations

Pu, Nan, et al.. (2024). Novel Class Discovery for Ultra-Fine-Grained Visual Categorization. Institutional Research Information System (Università degli Studi di Trento). 17679–17688. 4 indexed citations

Pu, Nan, et al.. (2024). Federated Generalized Category Discovery. Institutional Research Information System (Università degli Studi di Trento). 28741–28750. 7 indexed citations

Liu, Yu, et al.. (2024). PMGNet: Disentanglement and entanglement benefit mutually for compositional zero-shot learning. Computer Vision and Image Understanding. 249. 104197–104197.

Qin, Yalan, Nan Pu, Hanzhou Wu, & Nicu Sebe. (2024). Discriminative Anchor Learning for Efficient Multi-View Clustering. IEEE Transactions on Multimedia. 27. 1386–1396. 1 indexed citations

10.

Pu, Nan, et al.. (2023). Multi-Domain Lifelong Visual Question Answering via Self-Critical Distillation. Institutional Research Information System (Università degli Studi di Trento). 4747–4758. 2 indexed citations

11.

Pu, Nan, et al.. (2023). COCA: COllaborative CAusal Regularization for Audio-Visual Question Answering. Proceedings of the AAAI Conference on Artificial Intelligence. 37(11). 12995–13003. 12 indexed citations

12.

Pu, Nan, et al.. (2023). Dual selective knowledge transfer for few-shot classification. Applied Intelligence. 53(22). 27779–27789. 2 indexed citations

13.

Pu, Nan, Zhun Zhong, & Nicu Sebe. (2023). Dynamic Conceptional Contrastive Learning for Generalized Category Discovery. Institutional Research Information System (Università degli Studi di Trento). 7579–7588. 37 indexed citations

14.

Pu, Nan, et al.. (2023). FedVQA: Personalized Federated Visual Question Answering over Heterogeneous Scenes. Institutional Research Information System (Università degli Studi di Trento). 7796–7807. 4 indexed citations

15.

He, Kai, et al.. (2023). Few-shot and meta-learning methods for image understanding: a survey. International Journal of Multimedia Information Retrieval. 12(2). 18 indexed citations

16.

Pu, Nan, et al.. (2023). Identification of driving factors of algal growth in the South-to-North Water Diversion Project by Transformer-based deep learning. SHILAP Revista de lepidopterología. 2(3). 100184–100184. 14 indexed citations

17.

Guo, Yanming, et al.. (2022). VQA-BC: Robust Visual Question Answering Via Bidirectional Chaining. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 4833–4837. 2 indexed citations

18.

Georgiou, Theodoros, Sebastian Schmitt, Thomas Bäck, et al.. (2021). Comparison of deep learning and hand crafted features for mining simulation data. Leiden Repository (Leiden University). 1–8.

19.

Pu, Nan, Wei Chen, Yu Liu, Erwin M. Bakker, & Michael S. Lew. (2020). Dual Gaussian-based Variational Subspace Disentanglement for Visible-Infrared Person Re-Identification. Leiden Repository (Leiden University). 2149–2158. 72 indexed citations

20.

Chen, Wei, Nan Pu, Yu Liu, Erwin M. Bakker, & Michael S. Lew. (2019). Domain Uncertainty Based On Information Theory for Cross-Modal Hash Retrieval. 43–48. 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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