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Topic · Computer Vision and Pattern Recognition

Advanced Neural Network Applications

Advanced Neural Network Applications is a research topic within Computer Vision and Pattern Recognition. Science Explorer counts 52k research works in it since 1971. 9.5% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the application of deep learning, particularly convolutional neural networks, in computer vision tasks such as image recognition, object detection, and semantic segmentation. It covers various neural network architectures, model compression techniques, and their applications in fields like autonomous driving.

  • Deep Learning
  • Convolutional Neural Networks
  • Image Recognition
  • Object Detection
  • Semantic Segmentation
  • Neural Network Architectures
  • Computer Vision
  • Image Classification
  • Model Compression
  • Autonomous Driving
Research works
52k
fractional, since 1971
In the world top 10%
4.9k
per year above
Top-10% rate
9.5%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+120%
the tick is no change
Largest producer
China
62% of the 10 largest, 2022-now
Advanced Neural Network Applications

Which countries lead Advanced Neural Network Applications research?

By volume, China and India publish the most (17k and 3.8k works in 2022-now).

By volume, 2022-now

  1. 1 China 17k works
  2. 2 India 3.8k works
  3. 3 United States 2.6k works
  4. 4 South Korea 1k works
  5. 5 Germany 704 works
  6. 6 United Kingdom 661 works
  7. 7 Japan 550 works
  8. 8 Canada 426 works
  9. 9 Taiwan 386 works
  10. 10 Türkiye 376 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 61.6%India: 13.9%United States: 9.5%South Korea: 3.7%6 others listed: 11.2%62%largest
China17k · 61.6%India3,839 · 13.9%United States2,622 · 9.5%South Korea1,033 · 3.7%6 others listed3,102 · 11.2%

Shares of the rows listed above, not of the whole node.

Which institutions lead Advanced Neural Network Applications research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Advanced Neural Network Applications research, followed by Shanghai Jiao Tong University and Tsinghua University.

By volume, 2022-now

  1. 1 Chinese Academy of SciencesChina 611 works
  2. 2 Shanghai Jiao Tong UniversityChina 223 works
  3. 3 Tsinghua UniversityChina 222 works
  4. 4 National University of Defense TechnologyChina 209 works
  5. 5 Beihang UniversityChina 203 works
  6. 6 University of Electronic Science and Technology of ChinaChina 201 works
  7. 7 Zhejiang UniversityChina 187 works
  8. 8 Xidian UniversityChina 186 works
  9. 9 Beijing Institute of TechnologyChina 180 works
  10. 10 SRM Institute of Science and TechnologyIndia 169 works

Who are the leading researchers in Advanced Neural Network Applications?

The most-cited researchers publishing on Advanced Neural Network Applications include Kaiming He, Yoshua Bengio and Geoffrey E. Hinton.

  1. 1 Kaiming He Israel 562k citations
  2. 2 Yoshua Bengio Canada 476k citations
  3. 3 Geoffrey E. Hinton Canada 454k citations
  4. 4 Shaoqing Ren China 356k citations
  5. 5 Ross Girshick Israel 356k citations
  6. 6 Xiangyu Zhang 327k citations
  7. 7 Luigi Ferrucci United States 281k citations
  8. 8 Jian Sun 281k citations
  9. 9 Xiaogang Wang China 260k citations
  10. 10 Yann LeCun United States 256k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Advanced Neural Network Applications?

The most cited paper in Advanced Neural Network Applications is "Deep Residual Learning for Image Recognition" (Kaiming He, Xiangyu Zhang, Shaoqing Ren et al., 2016), with 229,585 citations. Next come "U-Net: Convolutional Networks for Biomedical Image Segmentation" (92,751) and "ImageNet classification with deep convolutional neural networks" (75,634).

Most cited papers
#PaperReleasedCitations
1 Deep Residual Learning for Image Recognition Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. Conference paperOpen access Jun 2016 229,585
2 U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger, Philipp F. Fischer, Thomas Brox · Lecture notes in computer science Conference paperOpen access 2015 92,751
3 ImageNet classification with deep convolutional neural networks Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton · Communications of the ACM Open access 24 May 2017 75,634
4 Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan, Andrew Zisserman · arXiv (Cornell University) PreprintOpen access 4 Sep 2014 75,434
5 Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren, Kaiming He, Ross Girshick et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence 6 Jun 2016 55,999
All 95 most cited papers, with filters and sorting

Where is Advanced Neural Network Applications research done?

The largest centres of Advanced Neural Network Applications research in 2022-now are Beijing (China), Shanghai (China), Xi'an (China) and Nanjing (China). Among places with at least 20 works in it, it is an unusually large share of all research in Shanghai and Fuxin.

Largest cities, 2022-now

  1. 1 BeijingChina 2.9k works
  2. 2 ShanghaiChina 1.2k works
  3. 3 Xi'anChina 943 works
  4. 4 NanjingChina 856 works
  5. 5 WuhanChina 781 works
  6. 6 GuangzhouChina 610 works
  7. 7 HangzhouChina 573 works
  8. 8 ChengduChina 560 works
  9. 9 ChangshaChina 509 works
  10. 10 SeoulSouth Korea 500 works

Where it is the local speciality

  1. Shanghai33.2 works7.3×
  2. FuxinCN · 39.6 works7.0×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Advanced Neural Network Applications than the world average.

See Advanced Neural Network Applications on the map

Where is the best place to study Advanced Neural Network Applications?

Among universities, judged by research, Hong Kong University of Science and Technology, Tsinghua University and Northwestern Polytechnical University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 19.42%fractional works in this field (log) →share of work in the world top 10% →Hong Kong University…Tsinghua UniversityNorthwestern Polytec…Xidian University
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Hong Kong University of Science and TechnologyChina 70.623.5%5.4×64 +71.3%
2Tsinghua UniversityChina 69.916.6%3.3×222 +44.3%
3Northwestern Polytechnical UniversityChina 68.216.5%5.1×168 +102.3%
4Xidian UniversityChina 68.013.8%8.9×186 +115.6%
5Princess Nourah bint Abdulrahman UniversitySaudi Arabia 67.231.0%2.5×18 +1099.6%
6Chinese University of Hong KongChina 67.021.4%2.6×54 +9.6%
7Wuhan UniversityChina 65.615.0%3.9×159 +130.0%
8Mohamed bin Zayed University of Artificial IntelligenceUnited Arab Emirates 65.423.4%19.7×23 +525.4%
9University of Hong KongChina 64.819.0%2.4×65 +204.9%
10Zhejiang UniversityChina 63.914.0%2.4×187 +97.6%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Advanced Neural Network Applications, country by country

Is Advanced Neural Network Applications research growing?

Output in 2022-2025 was 120% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Advanced Neural Network Applications.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Advanced Neural Network Applications as a percentage of every work published that year, 0.10% in 2024.

still counting0.10%0.20%Advanced Neural Network Applications0.16% in 202519801990200020102020

Fractional works of Advanced Neural Network Applications over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Advanced Neural Network Applications9.6% in 202520102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.

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