Basura Fernando

55 papers receiving 2.7k citations

Hit Papers

Modeling video evolution for action recognition2015202620182022201520162015100200300

Peers

Basura Fernando
Comparison fields: 5 of 120
  • Computer Vision and Pattern Recognition 2.4k
  • Artificial Intelligence 1.2k
  • Biomedical Engineering 546
  • Human-Computer Interaction 280
  • Radiology, Nuclear Medicine and Imaging 117
Replace Efstratios Gavves with:
Efstratios Gavves Netherlands
Zhaofan Qiu China
Yutaka Satoh Japan
Bjoern Andres Germany
Haizhou Ai China
Jing Pan China
Hirokatsu Kataoka Japan
James Ferryman United Kingdom
Honggang Zhang China
Angela Yao Singapore
Basura Fernando relative to Efstratios Gavves Netherlands Efstratios Gavves's profile →
Citations per field
00.5×1.5×
Efstratios Gavves · 1×
Citations per year

Countries citing papers authored by Basura Fernando

Since Specialization
Citations

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

Fields of papers citing papers by Basura Fernando

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Basura Fernando

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 6
3 0
4 1
5 1
6 2
7 1
8 19
9
Neural Feature Matching in Implicit 3D Representations
2
10 24
11 64
12 29
13
Deep Learning Based Decision Support System for Automated Diagnosis of Age-related Macular Degeneration (AMD)
6
14 221
15
Learning end-to-end video classification with rank-pooling
48
16
Dynamic Image Networks for Action Recognitionbreakdown →
376
17
Deep Learning for Automatic Detection and Classification of Microaneurysms, Hard and Soft Exudates, and Hemorrhages for Diabetic Retinopathy Diagnosis
8
18
Lost in the Past: Recognizing Locations Over Large Time Lags.
1
19 10
20
Effective use of frequent itemset mining for image classification
2

About Basura Fernando

Basura Fernando is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology, having authored 58 papers that have together received 2.8k indexed citations. Recurring topics across this work include Human Pose and Action Recognition (22 papers), Multimodal Machine Learning Applications (17 papers) and Advanced Image and Video Retrieval Techniques (16 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.4k citations), Human-Computer Interaction (280 citations) and Artificial Intelligence (1.2k citations). Basura Fernando has collaborated with scholars based in Australia, Singapore and Belgium. Frequent co-authors include Efstratios Gavves, Tinne Tuytelaars, Stephen Jay Gould, Hakan Bilen, Amir Ghodrati, José Oramas, Andrea Vedaldi, Xu Jia, Richard Hartley and Fatih Porikli. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Pattern Recognition.

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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