Hexiang Hu

4.1k total citations · 1 hit paper
28 papers, 1.1k citations indexed

About

Hexiang Hu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Electronic, Optical and Magnetic Materials. According to data from OpenAlex, Hexiang Hu has authored 28 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Computer Vision and Pattern Recognition, 20 papers in Artificial Intelligence and 4 papers in Electronic, Optical and Magnetic Materials. Recurrent topics in Hexiang Hu's work include Multimodal Machine Learning Applications (18 papers), Domain Adaptation and Few-Shot Learning (15 papers) and Advanced Image and Video Retrieval Techniques (10 papers). Hexiang Hu is often cited by papers focused on Multimodal Machine Learning Applications (18 papers), Domain Adaptation and Few-Shot Learning (15 papers) and Advanced Image and Video Retrieval Techniques (10 papers). Hexiang Hu collaborates with scholars based in United States, China and Canada. Hexiang Hu's co-authors include Fei Sha, Han-Jia Ye, De‐Chuan Zhan, Changhu Wang, Jiacheng Chen, Yuning Jiang, Hao Wu, Wei‐Lun Chao, Greg Mori and Zhiwei Deng and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision and Journal of Alloys and Compounds.

In The Last Decade

Hexiang Hu

28 papers receiving 1.1k 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.

Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions

2020 451 citations Han-Jia Ye, Hexiang Hu et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Hexiang Hu United States	15	773	729	108	56	49	28	1.1k
Junge Zhang China	16	332 0.4×	461 0.6×	39 0.4×	58 1.0×	26 0.5×	55	795
Xiaoyu Tao China	15	441 0.6×	376 0.5×	68 0.6×	53 0.9×	61 1.2×	28	801
Hongguang Zhang China	15	368 0.5×	388 0.5×	78 0.7×	69 1.2×	42 0.9×	26	650
Zhen Chen China	17	307 0.4×	317 0.4×	193 1.8×	58 1.0×	55 1.1×	85	860
Jongmin Kim South Korea	13	433 0.6×	202 0.3×	40 0.4×	29 0.5×	90 1.8×	26	779
Junjie Zhang China	13	381 0.5×	463 0.6×	47 0.4×	162 2.9×	39 0.8×	78	746
Jiawei Wu China	11	245 0.3×	439 0.6×	29 0.3×	108 1.9×	53 1.1×	49	693
Qi Dong China	10	264 0.3×	246 0.3×	55 0.5×	31 0.6×	43 0.9×	20	504
Lingqiao Li China	14	266 0.3×	399 0.5×	153 1.4×	118 2.1×	28 0.6×	44	678

Countries citing papers authored by Hexiang Hu

Since Specialization

Citations

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

Fields of papers citing papers by Hexiang Hu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hexiang Hu

This figure shows the co-authorship network connecting the top 25 collaborators of Hexiang Hu. A scholar is included among the top collaborators of Hexiang Hu 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 Hexiang Hu. Hexiang Hu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Hu, Hexiang, et al.. (2024). Magnetron Sputtered SnO₂ Layer Combined with NiCo−LDH Nanosheets for High‐Performance All‐Solid‐State Supercapacitors. Batteries & Supercaps. 7(8). 4 indexed citations

2.

Hu, Hexiang, Kelvin C. K. Chan, Yu-Chuan Su, et al.. (2024). Instruct-Imagen: Image Generation with Multi-modal Instruction. 4754–4763. 8 indexed citations

3.

Changpinyo, Soravit, Xi Chen, Hexiang Hu, et al.. (2023). PreSTU: Pre-Training for Scene-Text Understanding. 15224–15234. 8 indexed citations

4.

Chen, Yang, Hexiang Hu, Yi Luan, et al.. (2023). Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?. 14948–14968. 10 indexed citations

5.

Hu, Hexiang, et al.. (2023). Calculation example of full cycle carbon emission of super deep foundation pit and carbon reduction measures. 1(1). 3 indexed citations

6.

Hu, Hexiang, Jialun Li, Yi Jiang, et al.. (2023). Nickel–cobalt layered double hydroxide nanoflakes combined carbonized melamine sponge for high-performance supercapacitors and pressure sensors. Ionics. 29(10). 4285–4293. 8 indexed citations

7.

Hu, Hexiang, Ozan Şener, Fei Sha, & Vladlen Koltun. (2022). Drinking from a Firehose: Continual Learning with Web-scale Natural Language. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(5). 1–12. 5 indexed citations

8.

Chen, Wenhu, et al.. (2022). MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text. 5558–5570. 39 indexed citations

9.

Chen, Jiacheng, Hexiang Hu, Hao Wu, Yuning Jiang, & Changhu Wang. (2021). Learning the Best Pooling Strategy for Visual Semantic Embedding. 15784–15793. 171 indexed citations

10.

Ye, Han-Jia, Hexiang Hu, & De‐Chuan Zhan. (2021). Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning. International Journal of Computer Vision. 129(6). 1930–1953. 39 indexed citations

11.

Li, Yandong, Hexiang Hu, Dong Xuan, et al.. (2021). MosaicOS: A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 407–417. 17 indexed citations

12.

Zhang, Bowen, Hexiang Hu, Vihan Jain, Eugene Ie, & Fei Sha. (2020). Learning to Represent Image and Text with Denotation Graph. 823–839. 14 indexed citations

13.

Ye, Han-Jia, Hexiang Hu, De‐Chuan Zhan, & Fei Sha. (2019). Learning Classifier Synthesis for Generalized Few-Shot Learning. arXiv (Cornell University). 3 indexed citations

14.

Sun, Shaohua, et al.. (2019). Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation. arXiv (Cornell University). 32. 1–12. 36 indexed citations

15.

Nauata, Nelson, Hexiang Hu, Guang-Tong Zhou, et al.. (2019). Structured Label Inference for Visual Understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence. 42(5). 1–1. 7 indexed citations

16.

Ye, Han-Jia, Hexiang Hu, De‐Chuan Zhan, & Fei Sha. (2018). Learning Embedding Adaptation for Few-Shot Learning. arXiv (Cornell University). 33 indexed citations

17.

Sha, Fei, Hexiang Hu, & Wei‐Lun Chao. (2018). Cross-Dataset Adaptation for Visual Question Answering. 5716–5725. 30 indexed citations

18.

Hu, Hexiang, et al.. (2017). FastMask: Segment Multi-scale Object Candidates in One Shot. 2280–2288. 14 indexed citations

19.

Hu, Hexiang, Shiyi Lan, Yuning Jiang, Zhimin Cao, & Fei Sha. (2016). FastMask: Segment Object Multi-scale Candidates in One Shot.. arXiv (Cornell University). 1 indexed citations

20.

Hu, Hexiang, Guang-Tong Zhou, Zhiwei Deng, Zicheng Liao, & Greg Mori. (2016). Learning Structured Inference Neural Networks with Label Relations. 2960–2968. 80 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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