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.
Countries citing papers authored by Barbara Caputo
Since
Specialization
Citations
This map shows the geographic impact of Barbara Caputo'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 Barbara Caputo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Barbara Caputo more than expected).
This network shows the impact of papers produced by Barbara Caputo. 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 Barbara Caputo. The network helps show where Barbara Caputo may publish in the future.
Co-authorship network of co-authors of Barbara Caputo
This figure shows the co-authorship network connecting the top 25 collaborators of Barbara Caputo.
A scholar is included among the top collaborators of Barbara Caputo 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 Barbara Caputo. Barbara Caputo is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Caputo, Barbara, et al.. (2018). DeepNCM: Deep Nearest Class Mean Classifiers. International Conference on Learning Representations.16 indexed citations
7.
Carlucci, Fabio Maria, Paolo Russo, Tatiana Tommasi, & Barbara Caputo. (2018). Agnostic Domain Generalization.. arXiv (Cornell University).3 indexed citations
8.
Fornoni, Marco, Barbara Caputo, & Francesco Orabona. (2013). Multiclass Latent Locally Linear Support Vector Machines. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 229–244.13 indexed citations
9.
Orabona, Francesco, Jie Luo, & Barbara Caputo. (2012). Multi kernel learning with online-batch optimization. Journal of Machine Learning Research. 13(1). 227–253.34 indexed citations
10.
Clough, Paul, et al.. (2010). ImageCLEF: Experimental Evaluation in Visual Information Retrieval. Springer eBooks.72 indexed citations
11.
Pronobis, Andrzej, Marco Fornoni, Henrik I. Christensen, & Barbara Caputo. (2010). The Robot Vision Track at ImageCLEF 2010. Infoscience (Ecole Polytechnique Fédérale de Lausanne).6 indexed citations
Orabona, Francesco, Joseph Keshet, & Barbara Caputo. (2009). Bounded Kernel-Based Online Learning. Journal of Machine Learning Research. 10(92). 2643–2666.63 indexed citations
14.
Orabona, Francesco, Joseph Keshet, & Barbara Caputo. (2009). Bounded kernel-based perceptrons. Journal of Machine Learning Research.1 indexed citations
15.
Tommasi, Tatiana, Francesco Orabona, & Barbara Caputo. (2008). CLEF2008 Image Annotation Task: an SVM Confidence-Based Approach. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1174.5 indexed citations
16.
Tommasi, Tatiana, Francesco Orabona, & Barbara Caputo. (2007). CLEF2007 Image Annotation Task: an SVM-based Cue Integration Approach. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1173.18 indexed citations
17.
Caputo, Barbara, et al.. (2002). How to Combine Color and Shape Information for 3D Object Recognition: Kernels do the Trick. HAL (Le Centre pour la Communication Scientifique Directe). 15. 1399–1406.11 indexed citations
18.
Caputo, Barbara, Gyuri Dorkó, & Heinrich Niemann. (2002). An ultrametric approach to object recognition. HAL (Le Centre pour la Communication Scientifique Directe). 13–20.2 indexed citations
19.
Caputo, Barbara & H. Niemann. (2001). From Markov Random Fields to Associative Memories and Back: Spin-Glass Markov Random Fields.5 indexed citations
20.
Caputo, Barbara, et al.. (2001). A Novel Probabilistic Model for 3D Object Recognition: Spin-Glass Markov Random Fields. Vision Modeling and Visualization. 465–472.1 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.