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.
Deep Face Recognition
20153.0k citationsOmkar Parkhi, Andrea Vedaldi et al.profile →
Countries citing papers authored by Andrea Vedaldi
Since
Specialization
Citations
This map shows the geographic impact of Andrea Vedaldi'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 Andrea Vedaldi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrea Vedaldi more than expected).
This network shows the impact of papers produced by Andrea Vedaldi. 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 Andrea Vedaldi. The network helps show where Andrea Vedaldi may publish in the future.
Co-authorship network of co-authors of Andrea Vedaldi
This figure shows the co-authorship network connecting the top 25 collaborators of Andrea Vedaldi.
A scholar is included among the top collaborators of Andrea Vedaldi 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 Andrea Vedaldi. Andrea Vedaldi 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
1.
Chen, Honglie, Weidi Xie, Andrea Vedaldi, & Andrew Zisserman. (2020). Vggsound: A Large-Scale Audio-Visual Dataset. Oxford University Research Archive (ORA) (University of Oxford). 721–725.234 indexed citations breakdown →
2.
Ehrhardt, Sébastien, Oliver Groth, Áron Monszpart, et al.. (2020). RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces. Oxford University Research Archive (ORA) (University of Oxford). 33. 11202–11213.3 indexed citations
3.
Asano, Yuki M., Mandela Patrick, Christian Rupprecht, & Andrea Vedaldi. (2020). Labelling unlabelled videos from scratch with multi-modal self-supervision. Oxford University Research Archive (ORA) (University of Oxford). 33. 4660–4671.6 indexed citations
4.
Asano, Yuki M., Christian Rupprecht, & Andrea Vedaldi. (2020). A critical analysis of self-supervision, or what we can learn from a single image. Oxford University Research Archive (ORA) (University of Oxford).11 indexed citations
5.
Neverova, Natalia, David Novotný, & Andrea Vedaldi. (2019). Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels. Neural Information Processing Systems. 32. 918–926.14 indexed citations
6.
Asano, Yuki M., Christian Rupprecht, & Andrea Vedaldi. (2019). Surprising Effectiveness of Few-Image Unsupervised Feature Learning.. arXiv (Cornell University).3 indexed citations
7.
Ji, Xu, João F. Henriques, & Andrea Vedaldi. (2018). Invariant Information Distillation for Unsupervised Image Segmentation and Clustering. arXiv (Cornell University).21 indexed citations
8.
Hu, Jie, Li Shen, Samuel Albanie, Gang Sun, & Andrea Vedaldi. (2018). Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks. Oxford University Research Archive (ORA) (University of Oxford). 31. 9401–9411.88 indexed citations
9.
Bilen, Hakan, et al.. (2018). Modelling and unsupervised learning of symmetric deformable object categories. Edinburgh Research Explorer (University of Edinburgh). 31. 8178–8189.5 indexed citations
10.
Jakab, Tomáš, Ankush Gupta, Hakan Bilen, & Andrea Vedaldi. (2018). Conditional Image Generation for Learning the Structure of Visual Objects.. arXiv (Cornell University).7 indexed citations
11.
Rebuffi, Sylvestre-Alvise, Hakan Bilen, & Andrea Vedaldi. (2017). Learning multiple visual domains with residual adapters. Oxford University Research Archive (ORA) (University of Oxford). 30. 506–516.270 indexed citations
12.
Bertinetto, Luca, João F. Henriques, Jack Valmadre, Philip H. S. Torr, & Andrea Vedaldi. (2016). Learning feed-forward one-shot learners. Oxford University Research Archive (ORA) (University of Oxford). 29. 523–531.88 indexed citations
13.
Tsogkas, Stavros, Iasonas Kokkinos, George Papandreou, & Andrea Vedaldi. (2015). Semantic Part Segmentation with Deep Learning.. arXiv (Cornell University).13 indexed citations
14.
Jaderberg, Max, Karen Simonyan, Andrea Vedaldi, & Andrew Zisserman. (2015). Deep Structured Output Learning for Unconstrained Text Recognition. Oxford University Research Archive (ORA) (University of Oxford).98 indexed citations
15.
Simonyan, Karen, Andrea Vedaldi, & Andrew Zisserman. (2013). Deep Fisher Networks for Large-Scale Image Classification. Oxford University Research Archive (ORA) (University of Oxford). 26. 163–171.109 indexed citations
16.
Lempitsky, Victor, Andrea Vedaldi, & Andrew Zisserman. (2011). Pylon Model for Semantic Segmentation. Oxford University Research Archive (ORA) (University of Oxford). 24. 1485–1493.71 indexed citations
17.
Chandra, Siddhartha, Omkar Parkhi, C. V. Jawahar, et al.. (2010). Oxford-IIIT TRECVID 2010 - Notebook paper.. Oxford University Research Archive (ORA) (University of Oxford).1 indexed citations
18.
Blaschko, Matthew B., Andrea Vedaldi, & Andrew Zisserman. (2010). Simultaneous Object Detection and Ranking with Weak Supervision. Oxford University Research Archive (ORA) (University of Oxford). 23. 235–243.36 indexed citations
19.
Jain, Mihir, Omkar Parkhi, C. V. Jawahar, et al.. (2009). Oxford-IIIT TRECVID 2009 - Notebook Paper. Oxford University Research Archive (ORA) (University of Oxford).1 indexed citations
20.
Vedaldi, Andrea & Andrew Zisserman. (2009). Structured output regression for detection with partial truncation. Oxford University Research Archive (ORA) (University of Oxford). 22. 1928–1936.45 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.