Xiaohan Ding

4.8k total citations · 3 hit papers
16 papers, 2.7k citations indexed

About

Xiaohan Ding is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Xiaohan Ding has authored 16 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 10 papers in Artificial Intelligence and 3 papers in Electrical and Electronic Engineering. Recurrent topics in Xiaohan Ding's work include Advanced Neural Network Applications (10 papers), Domain Adaptation and Few-Shot Learning (8 papers) and Human Pose and Action Recognition (4 papers). Xiaohan Ding is often cited by papers focused on Advanced Neural Network Applications (10 papers), Domain Adaptation and Few-Shot Learning (8 papers) and Human Pose and Action Recognition (4 papers). Xiaohan Ding collaborates with scholars based in China, United Kingdom and United States. Xiaohan Ding's co-authors include Guiguang Ding, Jungong Han, Xiangyu Zhang, Jian Sun, Ningning Ma, Yuchen Guo, Sheng Tang, Jianchao Tan, Honghao Chen and Chenggang Yan and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Neural Networks and Learning Systems and Materials Letters.

In The Last Decade

Xiaohan Ding

13 papers receiving 2.6k 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.

RepVGG: Making VGG-style ConvNets Great Again

2021 1.5k citations Xiaohan Ding, Xiangyu Zhang et al. Rare & Special e-Zone (The Hong Kong University of Science and Technology) profile →
ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks

2019 512 citations Xiaohan Ding, Yuchen Guo et al. profile →
Diverse Branch Block: Building a Convolution as an Inception-like Unit

2021 287 citations Xiaohan Ding, Xiangyu Zhang et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Xiaohan Ding China	9	1.7k	715	389	258	257	16	2.7k
Xiyang Dai United Kingdom	17	2.4k 1.4×	967 1.4×	414 1.1×	242 0.9×	324 1.3×	24	3.5k
Zheng-Ning Liu China	7	1.3k 0.7×	583 0.8×	379 1.0×	165 0.6×	181 0.7×	10	2.5k
Meng-Hao Guo China	7	1.3k 0.7×	591 0.8×	382 1.0×	166 0.6×	180 0.7×	23	2.6k
Tai‐Jiang Mu China	18	1.6k 0.9×	568 0.8×	323 0.8×	143 0.6×	386 1.5×	56	2.8k
Song–Hai Zhang China	23	2.4k 1.4×	528 0.7×	626 1.6×	190 0.7×	240 0.9×	103	3.7k
Peize Sun Hong Kong	13	1.9k 1.1×	688 1.0×	274 0.7×	328 1.3×	313 1.2×	13	2.5k
Mengchen Liu United Kingdom	6	1.3k 0.8×	438 0.6×	301 0.8×	166 0.6×	220 0.9×	7	1.9k
Tianheng Cheng China	9	2.1k 1.2×	865 1.2×	495 1.3×	173 0.7×	264 1.0×	18	3.4k
Zhongyue Zhang China	8	1.7k 1.0×	905 1.3×	368 0.9×	152 0.6×	183 0.7×	23	2.9k

Countries citing papers authored by Xiaohan Ding

Since Specialization

Citations

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

Fields of papers citing papers by Xiaohan Ding

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaohan Ding

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

16 of 16 papers shown

Ding, Xiaohan, et al.. (2025). Designing Conversational AI to Support Think-Aloud Practice in Technical Interview Preparation for CS Students. 191–203.

Ding, Xiaohan, et al.. (2025). RefConv: Reparameterized Refocusing Convolution for Powerful ConvNets. IEEE Transactions on Neural Networks and Learning Systems. 36(6). 11617–11631.

Lin, Song, Yukang Chen, Shuai Yang, et al.. (2024). Low-Rank Approximation for Sparse Attention in Multi-Modal LLMs. 13763–13773. 2 indexed citations

Ding, Xiaohan, et al.. (2023). Manipulating Identical Filter Redundancy for Efficient Pruning on Deep and Complicated CNN. IEEE Transactions on Neural Networks and Learning Systems. 35(11). 16831–16844. 7 indexed citations

Ding, Xiaohan, Honghao Chen, Xiangyu Zhang, Jungong Han, & Guiguang Ding. (2022). RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 568–577. 48 indexed citations

Ding, Xiaohan, Xiangyu Zhang, Ningning Ma, et al.. (2021). RepVGG: Making VGG-style ConvNets Great Again. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 13728–13737. 1452 indexed citations breakdown →

Ding, Xiaohan, et al.. (2021). ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 4490–4500. 113 indexed citations

Ding, Xiaohan, Xiangyu Zhang, Jungong Han, & Guiguang Ding. (2021). Diverse Branch Block: Building a Convolution as an Inception-like Unit. 10881–10890. 287 indexed citations breakdown →

Ding, Xiaohan, et al.. (2021). Role of erbium in microstructure and mechanical properties of Sn58Bi42 solder alloy. Materials Letters. 305. 130745–130745. 7 indexed citations

10.

Wu, Xu, Xin Zhao, Lin Wang, et al.. (2020). Estimation of Water-cement Ratio of Hardened Cement Paste Based on Microstructure Image and Convolutional Neural Network. 348–352.

11.

Ding, Xiaohan, et al.. (2020). Removal and recovery of SO₂ and NO in oxy-fuel combustion flue gas by calcium-based slurry. SHILAP Revista de lepidopterología. 194. 4062–4062. 1 indexed citations

12.

Ding, Xiaohan, Yuchen Guo, Guiguang Ding, & Jungong Han. (2019). ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks. 1911–1920. 512 indexed citations breakdown →

13.

Ding, Xiaohan, Guiguang Ding, Yuchen Guo, Jungong Han, & Chenggang Yan. (2019). Approximated Oracle Filter Pruning for Destructive CNN Width Optimization. arXiv (Cornell University). 1607–1616. 26 indexed citations

14.

Guo, Yuchen, Guiguang Ding, Jungong Han, et al.. (2019). Dual-View Ranking with Hardness Assessment for Zero-Shot Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 8360–8367. 12 indexed citations

15.

Ding, Xiaohan, Guiguang Ding, Yuchen Guo, & Jungong Han. (2019). Centripetal SGD for Pruning Very Deep Convolutional Networks With Complicated Structure. 4938–4948. 119 indexed citations

16.

Ding, Xiaohan, Guiguang Ding, Jungong Han, & Sheng Tang. (2018). Auto-Balanced Filter Pruning for Efficient Convolutional Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 72 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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