Hao Tan

4.1k total citations · 1 hit paper
28 papers, 1.7k citations indexed

About

Hao Tan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Geophysics. According to data from OpenAlex, Hao Tan has authored 28 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Computer Vision and Pattern Recognition, 17 papers in Artificial Intelligence and 2 papers in Geophysics. Recurrent topics in Hao Tan's work include Multimodal Machine Learning Applications (16 papers), Topic Modeling (11 papers) and Natural Language Processing Techniques (8 papers). Hao Tan is often cited by papers focused on Multimodal Machine Learning Applications (16 papers), Topic Modeling (11 papers) and Natural Language Processing Techniques (8 papers). Hao Tan collaborates with scholars based in United States, China and Australia. Hao Tan's co-authors include Mohit Bansal, Jie Lei, Jaemin Cho, Jialu Li, Yubo Zhang, Jialu Li, Alan Kuntz, Ron Alterovitz, Haonan Chen and S. Huang and has published in prestigious journals such as Food Research International, Engineering Geology and Tunnelling and Underground Space Technology.

In The Last Decade

Hao Tan

25 papers receiving 1.7k 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.

LXMERT: Learning Cross-Modality Encoder Representations from Transformers

2019 1.3k citations Hao Tan, Mohit Bansal profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Hao Tan United States	12	1.4k	1.3k	40	36	35	28	1.7k
Justin Johnson United States	5	2.7k 1.9×	1.9k 1.5×	33 0.8×	38 1.1×	38 1.1×	7	3.0k
Oliver Groth United Kingdom	3	2.7k 1.9×	1.9k 1.5×	30 0.8×	34 0.9×	38 1.1×	8	3.0k
Linjie Li United States	19	1.6k 1.1×	1.0k 0.8×	50 1.3×	36 1.0×	16 0.5×	35	1.9k
Stanislaw Antol United States	4	2.3k 1.6×	1.9k 1.5×	62 1.6×	22 0.6×	46 1.3×	5	2.7k
Yulei Niu China	13	1.1k 0.8×	935 0.7×	18 0.5×	27 0.8×	36 1.0×	27	1.4k
Makarand Tapaswi Germany	14	1.1k 0.7×	526 0.4×	133 3.3×	27 0.8×	21 0.6×	39	1.2k
Kaihua Tang China	7	1.5k 1.0×	1.1k 0.9×	17 0.4×	21 0.6×	24 0.7×	14	1.7k
Anna Rohrbach United States	16	1.0k 0.7×	616 0.5×	67 1.7×	27 0.8×	24 0.7×	33	1.2k
Nan Ding China	10	938 0.7×	830 0.7×	39 1.0×	32 0.9×	14 0.4×	29	1.3k
Adriana Kovashka United States	16	903 0.6×	619 0.5×	24 0.6×	19 0.5×	13 0.4×	53	1.1k

Countries citing papers authored by Hao Tan

Since Specialization

Citations

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

Fields of papers citing papers by Hao Tan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hao Tan

This figure shows the co-authorship network connecting the top 25 collaborators of Hao Tan. A scholar is included among the top collaborators of Hao Tan 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 Hao Tan. Hao Tan 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

1.

Dai, Feng, et al.. (2025). A multi-scale CNN-Transformer hybrid network for microseismic signal arrival picking: Model analysis and engineering application. Engineering Geology. 353. 108109–108109.

2.

Liu, Jiyuan, Chuankun Li, Xinhang Wan, et al.. (2025). Large-scale Multi-view Tensor Clustering with Implicit Linear Kernels. 20727–20736. 1 indexed citations

3.

Wang, Xiaowei, Hao Tan, Lei Shi, et al.. (2025). Key aroma compounds and their perceptual interaction effects on floral aroma perception in two typical light-flavour baijiu (Qingcha Daqu and Xucha Daqu). Food Research International. 220. 117059–117059.

4.

Dai, Feng, et al.. (2025). An efficiently designed CNN-Transformer fusion network for automatic and real-time microseismic signal classification. Tunnelling and Underground Space Technology. 161. 106534–106534. 2 indexed citations

5.

Dai, Feng, et al.. (2025). Lightweight and Robust Cross-Domain Microseismic Signal Classification Framework with Bi-Classifier Adversarial Learning. Engineering. 56. 267–283. 1 indexed citations

6.

Xu, Zhan, et al.. (2025). Progressive Autoregressive Video Diffusion Models. 6312–6322.

7.

Kafle, Kushal, et al.. (2024). Building Vision-Language Models on Solid Foundations with Masked Distillation. 14216–14226. 1 indexed citations

8.

Tan, Hao, Zhixin Shu, Yi Zhou, et al.. (2024). Carve3D: Improving Multi-view Reconstruction Consistency for Diffusion Models with RL Finetuning. 6369–6379. 1 indexed citations

9.

Lai, Viet Dac, Hao Tan, Trung Bui, et al.. (2023). Boosting Punctuation Restoration with Data Generation and Reinforcement Learning. 2133–2137. 2 indexed citations

10.

Hong, Yicong, Ruiyi Zhang, Franck Dernoncourt, et al.. (2023). Learning Navigational Visual Representations with Semantic Map Supervision. 3032–3044. 9 indexed citations

11.

Cho, Jaemin, Jie Lei, Hao Tan, & Mohit Bansal. (2021). Unifying Vision-and-Language Tasks via Text Generation. arXiv (Cornell University). 1931–1942. 110 indexed citations

12.

He, Cheng, Hao Tan, S. Huang, & Ran Cheng. (2021). Efficient evolutionary neural architecture search by modular inheritable crossover. Swarm and Evolutionary Computation. 64. 100894–100894. 20 indexed citations

13.

Li, Jialu, Hao Tan, & Mohit Bansal. (2021). Improving Cross-Modal Alignment in Vision Language Navigation via Syntactic Information. 1041–1050. 19 indexed citations

14.

Tan, Hao & Mohit Bansal. (2020). Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervision. 2066–2080. 49 indexed citations

15.

Tan, Hao, et al.. (2020). Modality-Balanced Models for Visual Dialogue. Proceedings of the AAAI Conference on Artificial Intelligence. 34(5). 8091–8098. 12 indexed citations

16.

Chen, Haonan, Hao Tan, Alan Kuntz, Mohit Bansal, & Ron Alterovitz. (2020). Enabling Robots to Understand Incomplete Natural Language Instructions Using Commonsense Reasoning. 1963–1969. 30 indexed citations

17.

Tan, Hao & Mohit Bansal. (2019). LXMERT: Learning Cross-Modality Encoder Representations from Transformers. 5099–5110. 1307 indexed citations breakdown →

18.

Tan, Hao & Mohit Bansal. (2018). Object Ordering with Bidirectional Matchings for Visual Reasoning. 444–451. 5 indexed citations

19.

Tan, Hao, et al.. (2013). Exploring aesthetics factors of gesture interaction in vehicles: An empirical study of in-car music player. 1 indexed citations

20.

Tan, Hao. (1997). Tactual Display For Wearable Computing. Medical Entomology and Zoology. 84–89. 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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