Marco Toldo

593 total citations
12 papers, 375 citations indexed

About

Marco Toldo is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Marco Toldo has authored 12 papers receiving a total of 375 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 8 papers in Computer Vision and Pattern Recognition and 6 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Marco Toldo's work include Domain Adaptation and Few-Shot Learning (9 papers), COVID-19 diagnosis using AI (6 papers) and Multimodal Machine Learning Applications (6 papers). Marco Toldo is often cited by papers focused on Domain Adaptation and Few-Shot Learning (9 papers), COVID-19 diagnosis using AI (6 papers) and Multimodal Machine Learning Applications (6 papers). Marco Toldo collaborates with scholars based in Italy, Australia and South Korea. Marco Toldo's co-authors include Pietro Zanuttigh, Umberto Michieli, Mete Özay, Gianluca Agresti, Alberto Rigon, Barbara Caputo, Marco Ciccone, Marco Minella, Luca Carena and Davide Vione and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Chemosphere.

In The Last Decade

Marco Toldo

12 papers receiving 372 citations

Peers

Marco Toldo
Lukas Hoyer Switzerland
Fan Ma China
Pascal Mettes Netherlands
Rafael de Oliveira Werneck Brazil
Lian Xu Australia
Yurong Guo China
Kunyang Sun China
Mennatullah Siam Canada
Tao Gong China
Lukas Hoyer Switzerland
Marco Toldo
Citations per year, relative to Marco Toldo Marco Toldo (= 1×) peers Lukas Hoyer

Countries citing papers authored by Marco Toldo

Since Specialization
Citations

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

Fields of papers citing papers by Marco Toldo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marco Toldo

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

All Works

12 of 12 papers shown
1.
Toldo, Marco, Umberto Michieli, & Pietro Zanuttigh. (2024). Learning With Style: Continual Semantic Segmentation Across Tasks and Domains. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(11). 7434–7450. 5 indexed citations
2.
Michieli, Umberto, et al.. (2023). Road scenes segmentation across different domains by disentangling latent representations. The Visual Computer. 40(2). 811–830. 2 indexed citations
3.
Toldo, Marco, et al.. (2023). Asynchronous Federated Continual Learning. Padua Research Archive (University of Padova). 5055–5063. 23 indexed citations
4.
Toldo, Marco, et al.. (2023). Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 444–454. 23 indexed citations
5.
Toldo, Marco & Mete Özay. (2022). Bring Evanescent Representations to Life in Lifelong Class Incremental Learning. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 16711–16720. 26 indexed citations
6.
Michieli, Umberto, Marco Toldo, & Mete Özay. (2022). Federated Learning via Attentive Margin of Semantic Feature Representations. IEEE Internet of Things Journal. 10(2). 1517–1535. 7 indexed citations
7.
Toldo, Marco, Umberto Michieli, & Pietro Zanuttigh. (2021). Unsupervised domain adaptation in semantic segmentation via orthogonal and clustered embeddings. Research Padua Archive (University of Padua). 39 indexed citations
8.
Michieli, Umberto, et al.. (2021). RECALL: Replay-based Continual Learning in Semantic Segmentation. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 7006–7015. 77 indexed citations
9.
Toldo, Marco, Umberto Michieli, Gianluca Agresti, & Pietro Zanuttigh. (2020). Unsupervised domain adaptation for mobile semantic segmentation based on cycle consistency and feature alignment. Image and Vision Computing. 95. 103889–103889. 31 indexed citations
10.
Toldo, Marco, et al.. (2020). Unsupervised Domain Adaptation in Semantic Segmentation: A Review. SHILAP Revista de lepidopterología. 8(2). 35–35. 119 indexed citations
11.
Toldo, Marco, et al.. (2020). Unsupervised Domain Adaptation in Semantic Segmentation: a Review. arXiv (Cornell University). 11 indexed citations
12.

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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2026