Hao Lü

5.9k citations
124 papers · 4.0k · 2 hit papers · h-index 32

Impact in

Papers in

Hao Lü

110 papers receiving 3.9k citations

Hao Lü's Hit Papers

Learning to Upsample by Learning to Sample 2023 · 382 citations
3820+2+4Years since publication100200300

Peers

Hao Lü
Comparison fields: 5 of 132
  • Computer Vision and Pattern Recognition 1.7k
  • Analytical Chemistry 395
  • Media Technology 361
  • Plant Science 1.3k
  • Ecology 648
Replace Ji‐Xiang Du with:
Ji‐Xiang Du China
Soumik Sarkar United States
Paolo Remagnino United Kingdom
Feras Dayoub Australia
Shiv Ram Dubey India
Juan I. Nieto Switzerland
Yu Sun China
Chris McCool Australia
Artzai Picón Spain
Xiangjun Zou China
Hao Lü relative to Ji‐Xiang Du China Ji‐Xiang Du's profile →
Citations per field
00.5×2×2.8×
Ji‐Xiang Du · 1×
Citations per year

Countries citing papers authored by Hao Lü

Since Specialization
Citations

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

Fields of papers citing papers by Hao Lü

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Hao Lü, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Hao Lü Line = papers co-authored together Hao Lü links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 124 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Learning to Upsample by Learning to Sample
Hit paper breakdown →
2023382
2
Machine Learning at Facebook: Understanding Inference at the Edge
Hit paper breakdown →
2019323
3 2018246
4 2017216
5 2018152
6 2019133
7 2019124
8 2019120
9 201695
10 201886
11 202080
12 202075
13 201973
14 201673
15 202169
16 202267
17 202066
18 202163
19 201562
20 201959

About Hao Lü

Hao Lü is a scholar working on Computer Vision and Pattern Recognition, Plant Science, Artificial Intelligence, Ecology and Analytical Chemistry, having authored 124 papers that have together received 4.0k indexed citations. Recurring topics across this work include Smart Agriculture and AI (33 papers), Remote Sensing in Agriculture (18 papers), Video Surveillance and Tracking Methods (17 papers), Advanced Neural Network Applications (13 papers), Domain Adaptation and Few-Shot Learning (11 papers), Advanced Image and Video Retrieval Techniques (11 papers), Spectroscopy and Chemometric Analyses (10 papers) and Visual Attention and Saliency Detection (10 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.7k citations), Analytical Chemistry (395 citations), Media Technology (361 citations), Plant Science (1.3k citations) and Ecology (648 citations). Hao Lü has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Zhiguo Cao, Chunhua Shen, Yang Xiao, Wenze Liu, Hongtao Fu, Haipeng Xiong, Ke Xian, Yanjun Zhu, Liang Liu and Chengxin Liu. Their work appears in journals such as Plant Phenomics, Computers and Electronics in Agriculture, Plant Methods, Frontiers in Plant Science and IEEE Transactions on Circuits and Systems for Video Technology.

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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