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Visual Attention and Saliency Detection

Visual Attention and Saliency Detection is a research topic within Computer Vision and Pattern Recognition. Science Explorer counts 17k research works in it since 1953. 19.2% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on computational modeling and detection of visual saliency, including topics such as saliency detection, visual attention, deep learning for salient object detection, analysis of eye movements, image and video segmentation, and the interplay between bottom-up and top-down attention mechanisms.

  • Saliency Detection
  • Visual Attention
  • Salient Object Detection
  • Deep Learning
  • Eye Movements
  • Image Segmentation
  • Video Object Segmentation
  • Neural Networks
  • Bottom-Up Attention
  • Top-Down Attention
Research works
17k
fractional, since 1953
In the world top 10%
3.2k
per year above
Top-10% rate
19.2%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+39%
the tick is no change

Which countries lead Visual Attention and Saliency Detection research?

By volume, China and the United States publish the most (2.3k and 528 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 2.3k works
  2. 2 United States 528 works
  3. 3 India 208 works
  4. 4 United Kingdom 157 works
  5. 5 Germany 153 works
  6. 6 Japan 151 works
  7. 7 South Korea 132 works
  8. 8 Canada 93 works
  9. 9 France 76 works
  10. 10 Italy 67 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: 59.6%United States: 13.6%India: 5.4%United Kingdom: 4.1%6 others listed: 17.3%60%largest
China2,312 · 59.6%United States528 · 13.6%India208 · 5.4%United Kingdom157 · 4.1%6 others listed672 · 17.3%

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

Which institutions lead Visual Attention and Saliency Detection research?

By volume in 2022–2025, Shanghai Jiao Tong University publishes the most Visual Attention and Saliency Detection research, followed by Xidian University and Tsinghua University.

Who are the leading researchers in Visual Attention and Saliency Detection?

The most-cited researchers publishing on Visual Attention and Saliency Detection include Andrew Zisserman, Li Fei-Fei and Serge Belongie.

  1. 1 Andrew Zisserman United Kingdom 25k citations
  2. 2 Li Fei-Fei United States 17k citations
  3. 3 Serge Belongie United States 14k citations
  4. 4 Jitendra Malik United States 12k citations
  5. 5 Alexander C. Berg United States 9.9k citations
  6. 6 Wei Liu China 9.7k citations
  7. 7 Luc Van Gool Switzerland 8.5k citations
  8. 8 Alan Yuille United States 8.4k citations
  9. 9 Ming–Hsuan Yang United States 7.4k citations
  10. 10 Bernt Schiele Germany 6.9k citations

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

Where is Visual Attention and Saliency Detection research done?

The largest centres of Visual Attention and Saliency Detection research in 2022–2025 are Beijing (China), Shanghai (China), Xi'an (China) and Nanjing (China).

Largest cities, 2022–2025

  1. 1 Beijing China 377 works
  2. 2 Shanghai China 194 works
  3. 3 Xi'an China 145 works
  4. 4 Nanjing China 124 works
  5. 5 Hangzhou China 111 works
  6. 6 Wuhan China 99 works
  7. 7 Tianjin China 90 works
  8. 8 Chengdu China 73 works
  9. 9 Seoul South Korea 72 works
  10. 10 Guangzhou China 67 works
See Visual Attention and Saliency Detection on the map

Where is the best place to study Visual Attention and Saliency Detection?

Among universities, judged by research, Xidian University, City University of Hong Kong and Dalian University of Technology 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%60%mean 35.94%fractional works in this node (log) →share in the world top 10% →Xidian University: 37, 30.4%City University of Hong Kong: 16, 44.4%Dalian University of Technology: 30, 39.3%Hong Kong University of Science and Technology: 10, 54.4%Beihang University: 31, 26.5%Nanyang Technological University: 22, 39.3%Anhui University: 23, 27.2%Hangzhou Dianzi University: 20, 20.9%Zhejiang University of Science and Technology: 12, 32.8%Nankai University: 20, 44.2%Hong Kong University…City University of H…Dalian University of…Xidian University
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Xidian UniversityChina 75.730.4%11.2×37 +286.5%
2 City University of Hong KongHong Kong 70.644.4%6.0×16 +471.5%
3 Dalian University of TechnologyChina 69.539.3%7.0×30 +222.8%
4 Hong Kong University of Science and TechnologyHong Kong 68.554.4%5.7×10 +188.0%
5 Beihang UniversityChina 60.526.5%6.3×31 +241.1%
6 Nanyang Technological UniversitySingapore 59.339.3%6.2×22 +11.4%
7 Anhui UniversityChina 59.127.2%11.3×23
8 Hangzhou Dianzi UniversityChina 59.020.9%13.5×20 +131.0%
9 Zhejiang University of Science and TechnologyChina 58.832.8%14.8×12 +95.7%
10 Nankai UniversityChina 57.944.2%6.7×20

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Visual Attention and Saliency Detection research growing?

Output in 2018–2022 was 39% higher than in 2013–2017, peaking in 2023. The fastest-growing topics are Visual Attention and Saliency Detection.

19801990200020102020
grewheldshrank

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

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.