Xintong Han

5.4k total citations · 4 hit papers
36 papers, 2.8k citations indexed

About

Xintong Han is a scholar working on Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics and Artificial Intelligence. According to data from OpenAlex, Xintong Han has authored 36 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Computer Vision and Pattern Recognition, 9 papers in Statistical and Nonlinear Physics and 7 papers in Artificial Intelligence. Recurrent topics in Xintong Han's work include Chaos control and synchronization (8 papers), Generative Adversarial Networks and Image Synthesis (5 papers) and Chaos-based Image/Signal Encryption (5 papers). Xintong Han is often cited by papers focused on Chaos control and synchronization (8 papers), Generative Adversarial Networks and Image Synthesis (5 papers) and Chaos-based Image/Signal Encryption (5 papers). Xintong Han collaborates with scholars based in China, United States and Japan. Xintong Han's co-authors include Larry S. Davis, Vlad I. Morariu, Peng Zhou, Matthew R. Scott, Weilin Huang, Xun Wang, Mingfei Gao, Chun-Fu Chen, Ching‐Yung Lin and Ruichi Yu and has published in prestigious journals such as PLoS ONE, Information Sciences and IEEE Transactions on Cybernetics.

In The Last Decade

Xintong Han

35 papers receiving 2.7k citations

Hit Papers

Multi-Similarity Loss With General Pair Weighting for Dee... 2017 2026 2020 2023 2019 2018 2018 2017 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. Multi-Similarity Loss With General Pair Weighting for Deep Metric Learning
    2019 493 citations Xun Wang, Xintong Han et al. profile →
  2. NISP: Pruning Networks Using Neuron Importance Score Propagation
    2018 470 citations Ruichi Yu, Ang Li et al. profile →
  3. Learning Rich Features for Image Manipulation Detection
    2018 438 citations Peng Zhou, Xintong Han et al. profile →
  4. Two-Stream Neural Networks for Tampered Face Detection
    2017 384 citations Peng Zhou, Xintong Han et al. profile →

Peers

Xintong Han
Dacheng Tao Australia
Rushi Lan China
Erik B. Sudderth United States
Shiyu Chang United States
Kun Zhan China
Thomas Navin Lal Germany
Fu Jie Huang China
Hongyang Chao China
Bernhard Nessler Austria
Yun-Qing Shi United States
Dacheng Tao Australia
Xintong Han
Citations per year, relative to Xintong Han Xintong Han (= 1×) peers Dacheng Tao

Countries citing papers authored by Xintong Han

Since Specialization
Citations

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

Fields of papers citing papers by Xintong Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xintong Han

This figure shows the co-authorship network connecting the top 25 collaborators of Xintong Han. A scholar is included among the top collaborators of Xintong Han 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 Xintong Han. Xintong Han 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.
Xing, Zhen, Xintong Han, Zhi-Qi Cheng, et al.. (2025). StableAnimator: High-Quality Identity-Preserving Human Image Animation. 21096–21106. 1 indexed citations
2.
Han, Xintong, et al.. (2023). TWO DISCRETE MEMRISTIVE CHAOTIC MAPS AND ITS DSP IMPLEMENTATION. Fractals. 31(6). 21 indexed citations
3.
4.
Wang, Junke, Zuxuan Wu, Jingjing Chen, et al.. (2022). ObjectFormer for Image Manipulation Detection and Localization. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2354–2363. 101 indexed citations
5.
Han, Xintong, et al.. (2022). A new set of hyperchaotic maps based on modulation and coupling. The European Physical Journal Plus. 137(4). 47 indexed citations
6.
Han, Xintong, Yongfeng Zhao, Liying Zhu, et al.. (2021). Association mapping for general combining ability with yield, plant height and ear height using F1 population in maize. PLoS ONE. 16(10). e0258327–e0258327. 3 indexed citations
7.
Han, Xintong, Jun Mou, Tianming Liu, & Yinghong Cao. (2021). A new fractional-order 2D discrete chaotic map and its DSP implement. The European Physical Journal Special Topics. 230(21-22). 3913–3925. 8 indexed citations
8.
Han, Xintong, et al.. (2021). Action-guided 3D Human Motion Prediction. Neural Information Processing Systems. 34. 2 indexed citations
9.
Liu, Tianming, Jun Mou, Li Xiong, et al.. (2021). Hyperchaotic maps of a discrete memristor coupled to trigonometric function. Physica Scripta. 96(12). 125242–125242. 36 indexed citations
10.
Wang, Xun, et al.. (2019). Multi-Similarity Loss With General Pair Weighting for Deep Metric Learning. 5017–5025. 493 indexed citations breakdown →
11.
Zhou, Peng, Xintong Han, Vlad I. Morariu, & Larry S. Davis. (2018). Learning Rich Features for Image Manipulation Detection. 1053–1061. 438 indexed citations breakdown →
12.
Yu, Ruichi, Ang Li, Chun-Fu Chen, et al.. (2018). NISP: Pruning Networks Using Neuron Importance Score Propagation. 9194–9203. 470 indexed citations breakdown →
13.
Zhou, Yan, et al.. (2018). 3D shape classification and retrieval based on polar view. Information Sciences. 474. 205–220. 15 indexed citations
14.
Han, Xintong, Zuxuan Wu, Phoenix X. Huang, et al.. (2017). Automatic Spatially-Aware Fashion Concept Discovery. 1472–1480. 134 indexed citations
15.
Zhou, Peng, Xintong Han, Vlad I. Morariu, & Larry S. Davis. (2017). Two-Stream Neural Networks for Tampered Face Detection. 1831–1839. 384 indexed citations breakdown →
16.
Han, Xintong, Yunqing Wang, Yaowu Xu, & Jim Bankoski. (2016). Machine Learning-based Early Termination in Prediction Block Decomposition for VP9. Electronic Imaging. 28(2). 1–6. 1 indexed citations
17.
Zhang, Fan, et al.. (2015). Automatic Design Method and Application in Complex Ship Block Lifting. 115–122. 2 indexed citations
18.
Han, Xintong, Chongyang Zhang, Weiyao Lin, et al.. (2015). Tree-Based Visualization and Optimization for Image Collection. IEEE Transactions on Cybernetics. 46(6). 1286–1300. 12 indexed citations
19.
Han, Xintong, et al.. (2012). Periodic motion detection with ROI-based similarity measure and extrema-based reference-frame selection. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. 1–4. 5 indexed citations
20.
Han, Xintong, et al.. (2012). Periodic motion detection with ROI-based similarity measure and extrema-based reference selection. IEEE Transactions on Consumer Electronics. 58(3). 947–954. 7 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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