Naila Murray

2.4k total citations · 1 hit paper
19 papers, 1.3k citations indexed

About

Naila Murray is a scholar working on Computer Vision and Pattern Recognition, Cognitive Neuroscience and Artificial Intelligence. According to data from OpenAlex, Naila Murray has authored 19 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 5 papers in Cognitive Neuroscience and 5 papers in Artificial Intelligence. Recurrent topics in Naila Murray's work include Visual Attention and Saliency Detection (7 papers), Advanced Image and Video Retrieval Techniques (6 papers) and Image Retrieval and Classification Techniques (6 papers). Naila Murray is often cited by papers focused on Visual Attention and Saliency Detection (7 papers), Advanced Image and Video Retrieval Techniques (6 papers) and Image Retrieval and Classification Techniques (6 papers). Naila Murray collaborates with scholars based in France, Spain and United States. Naila Murray's co-authors include Florent Perronnin, Luca Marchesotti, Xavier Otazu, C. Alejandro Párraga, María Vanrell, Saumya Jetley, Eleonora Vig, Hervé Jeǵou, Philippe-Henri Gosselin and Piotr Koniusz and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision and Pattern Recognition Letters.

In The Last Decade

Naila Murray

19 papers receiving 1.3k citations

Hit Papers

AVA: A large-scale database for aesthetic visual analysis 2012 2026 2016 2021 2012 100 200 300 400 500
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.

  1. AVA: A large-scale database for aesthetic visual analysis
    2012 559 citations Naila Murray, Luca Marchesotti et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Naila Murray France 12 1.2k 328 183 151 138 19 1.3k
Luca Marchesotti Italy 11 1.2k 1.0× 439 1.3× 85 0.5× 108 0.7× 124 0.9× 27 1.4k
Aykut Erdem Türkiye 20 1.4k 1.2× 108 0.3× 338 1.8× 109 0.7× 189 1.4× 68 1.6k
Ulrich Engelke Australia 17 801 0.7× 234 0.7× 74 0.4× 41 0.3× 162 1.2× 87 1.1k
Weihua Xiong China 13 775 0.7× 75 0.2× 124 0.7× 68 0.5× 164 1.2× 45 906
Chenlei Guo United States 10 1.2k 1.1× 215 0.7× 225 1.2× 309 2.0× 157 1.1× 27 1.5k
Simone Frintrop Germany 22 1.5k 1.3× 324 1.0× 98 0.5× 267 1.8× 98 0.7× 62 1.7k
Nigel Crook United Kingdom 12 495 0.4× 127 0.4× 173 0.9× 161 1.1× 29 0.2× 32 723
Patrick Le Callet France 10 786 0.7× 212 0.6× 37 0.2× 123 0.8× 154 1.1× 16 879
Jonathan Harel United States 4 828 0.7× 251 0.8× 28 0.2× 241 1.6× 92 0.7× 7 950
Alexander C. Loui United States 23 1.4k 1.2× 168 0.5× 258 1.4× 14 0.1× 88 0.6× 91 1.6k

Countries citing papers authored by Naila Murray

Since Specialization
Citations

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

Fields of papers citing papers by Naila Murray

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Naila Murray

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

All Works

19 of 19 papers shown
1.
2.
Zhang, Shan, Lei Wang, Naila Murray, & Piotr Koniusz. (2022). Kernelized Few-shot Object Detection with Efficient Integral Aggregation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 19185–19194. 48 indexed citations
3.
Ionescu, Bogdan, Wilma Bainbridge, & Naila Murray. (2022). Human Perception of Visual Information. 6 indexed citations
4.
Sharma, Vivek, Naila Murray, Diane Larlus, et al.. (2021). Unsupervised Meta-Domain Adaptation for Fashion Retrieval. 1347–1356. 10 indexed citations
5.
Murray, Naila, et al.. (2019). A Detect-Then-Retrieve Model for Multi-Domain Fashion Item Retrieval. 18 indexed citations
6.
Murray, Naila. (2019). PFAGAN: An Aesthetics-Conditional GAN for Generating Photographic Fine Art. 3333–3341. 2 indexed citations
7.
Jetley, Saumya, Naila Murray, & Eleonora Vig. (2016). End-to-End Saliency Mapping via Probability Distribution Prediction. elib (German Aerospace Center). 5753–5761. 85 indexed citations
8.
Murray, Naila, Hervé Jeǵou, Florent Perronnin, & Andrew Zisserman. (2016). Interferences in Match Kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence. 39(9). 1797–1810. 14 indexed citations
9.
Gordo, Albert, et al.. (2015). LEWIS: Latent Embeddings for Word Images and Their Semantics. 1242–1250. 10 indexed citations
10.
Murray, Naila & Florent Perronnin. (2014). Generalized Max Pooling. 2473–2480. 138 indexed citations
11.
Marchesotti, Luca, Naila Murray, & Florent Perronnin. (2014). Discovering Beautiful Attributes for Aesthetic Image Analysis. International Journal of Computer Vision. 113(3). 246–266. 69 indexed citations
12.
Gosselin, Philippe-Henri, Naila Murray, Hervé Jeǵou, & Florent Perronnin. (2014). Revisiting the Fisher vector for fine-grained classification. Pattern Recognition Letters. 49. 92–98. 70 indexed citations
13.
Murray, Naila, María Vanrell, Xavier Otazu, & C. Alejandro Párraga. (2013). Low-Level Spatiochromatic Grouping for Saliency Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 35(11). 2810–2816. 15 indexed citations
14.
Murray, Naila, Luca Marchesotti, & Florent Perronnin. (2012). AVA: A large-scale database for aesthetic visual analysis. 2408–2415. 559 indexed citations breakdown →
15.
Murray, Naila, Luca Marchesotti, & Florent Perronnin. (2012). Learning to rank images using semantic and aesthetic labels. 110.1–110.10. 10 indexed citations
16.
Murray, Naila, et al.. (2012). Toward automatic and flexible concept transfer. Computers & Graphics. 36(6). 622–634. 10 indexed citations
17.
Murray, Naila, María Vanrell, Xavier Otazu, & C. Alejandro Párraga. (2011). Saliency estimation using a non-parametric low-level vision model. Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona). 433–440. 231 indexed citations
18.
Murray, Naila, et al.. (2011). Towards automatic concept transfer. 167–176. 11 indexed citations
19.
Murray, Naila & Erik Rosenthal. (1987). Inferencing on an arbitrary set of links. 416–423. 1 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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