Xiaohan Lan

783 total citations · 1 hit paper
7 papers, 412 citations indexed

About

Xiaohan Lan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Xiaohan Lan has authored 7 papers receiving a total of 412 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 4 papers in Artificial Intelligence and 1 paper in Molecular Biology. Recurrent topics in Xiaohan Lan's work include Multimodal Machine Learning Applications (6 papers), Human Pose and Action Recognition (4 papers) and Video Analysis and Summarization (4 papers). Xiaohan Lan is often cited by papers focused on Multimodal Machine Learning Applications (6 papers), Human Pose and Action Recognition (4 papers) and Video Analysis and Summarization (4 papers). Xiaohan Lan collaborates with scholars based in China and United States. Xiaohan Lan's co-authors include Zhichun Wang, Qingsong Lv, Yu Zhang, Wenwu Zhu, Xin Wang, Yitian Yuan, Zhi Wang, Hong Chen, Long Chen and Lin Ma and has published in prestigious journals such as ACM Transactions on Multimedia Computing Communications and Applications, Proceedings of the 30th ACM International Conference on Multimedia and Proceedings of the AAAI Conference on Artificial Intelligence.

In The Last Decade

Xiaohan Lan

7 papers receiving 400 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.

Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks

2018 345 citations Zhichun Wang, Qingsong Lv 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 Lan China	6	361	181	101	26	25	7	412
Shib Sankar Dasgupta United States	5	284 0.8×	76 0.4×	58 0.6×	17 0.7×	34 1.4×	8	297
Octavian-Eugen Ganea Switzerland	6	419 1.2×	125 0.7×	43 0.4×	38 1.5×	38 1.5×	9	439
Swayambhu Nath Ray India	2	253 0.7×	73 0.4×	52 0.5×	15 0.6×	30 1.2×	2	264
Siwei Rao China	3	343 1.0×	104 0.6×	36 0.4×	18 0.7×	43 1.7×	6	364
Jatin Chauhan India	3	344 1.0×	70 0.4×	52 0.5×	24 0.9×	38 1.5×	10	376
Xiaotao Gu United States	9	349 1.0×	57 0.3×	54 0.5×	40 1.5×	69 2.8×	19	378
Xisen Jin United States	5	400 1.1×	50 0.3×	89 0.9×	12 0.5×	48 1.9×	7	427
Mojtaba Nayyeri Germany	10	279 0.8×	55 0.3×	55 0.5×	27 1.0×	38 1.5×	25	305
Deepak Nathani United States	5	360 1.0×	70 0.4×	53 0.5×	24 0.9×	44 1.8×	6	399
Mosha Chen China	13	509 1.4×	76 0.4×	47 0.5×	59 2.3×	67 2.7×	19	543

Countries citing papers authored by Xiaohan Lan

Since Specialization

Citations

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

Fields of papers citing papers by Xiaohan Lan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaohan Lan

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

7 of 7 papers shown

1.

Lan, Xiaohan, Yitian Yuan, Hong Chen, et al.. (2023). Curriculum Multi-Negative Augmentation for Debiased Video Grounding. Proceedings of the AAAI Conference on Artificial Intelligence. 37(1). 1213–1221. 10 indexed citations

2.

Wang, Xin, et al.. (2023). Curriculum-Listener: Consistency- and Complementarity-Aware Audio-Enhanced Temporal Sentence Grounding. 3117–3128. 6 indexed citations

3.

Wang, Xin, et al.. (2023). Mixup-Augmented Temporally Debiased Video Grounding with Content-Location Disentanglement. 4450–4459. 7 indexed citations

4.

Lan, Xiaohan, Yitian Yuan, Xin Wang, Zhi Wang, & Wenwu Zhu. (2022). A Survey on Temporal Sentence Grounding in Videos. ACM Transactions on Multimedia Computing Communications and Applications. 19(2). 1–33. 29 indexed citations

5.

Lan, Xiaohan, Yitian Yuan, Xin Wang, et al.. (2022). A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach. ACM Transactions on Multimedia Computing Communications and Applications. 19(6). 1–23. 12 indexed citations

6.

Wang, Xin, Xiaohan Lan, & Wenwu Zhu. (2022). Video Grounding and Its Generalization. Proceedings of the 30th ACM International Conference on Multimedia. 7377–7379. 3 indexed citations

7.

Wang, Zhichun, Qingsong Lv, Xiaohan Lan, & Yu Zhang. (2018). Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks. 349–357. 345 indexed citations breakdown →

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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