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.
Going deeper with convolutions
201530.6k citationsChristian Szegedy, Wei Liu et al.profile →
Traffic sign recognition with multi-scale Convolutional Networks
Countries citing papers authored by Pierre Sermanet
Since
Specialization
Citations
This map shows the geographic impact of Pierre Sermanet'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 Pierre Sermanet with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pierre Sermanet more than expected).
This network shows the impact of papers produced by Pierre Sermanet. 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 Pierre Sermanet. The network helps show where Pierre Sermanet may publish in the future.
Co-authorship network of co-authors of Pierre Sermanet
This figure shows the co-authorship network connecting the top 25 collaborators of Pierre Sermanet.
A scholar is included among the top collaborators of Pierre Sermanet 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 Pierre Sermanet. Pierre Sermanet is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Dwibedi, Debidatta, Pierre Sermanet, & Jonathan Tompson. (2018). Temporal Reasoning in Videos Using Convolutional Gated Recurrent Units. Computer Vision and Pattern Recognition. 1111–1116.12 indexed citations
7.
Sermanet, Pierre, Corey Lynch, Yevgen Chebotar, et al.. (2018). Time-Contrastive Networks: Self-Supervised Learning from Video. 1134–1141.305 indexed citations breakdown →
Sermanet, Pierre, Andrea Frome, & Esteban Real. (2015). Attention for fine-grained categorization. International Conference on Learning Representations.15 indexed citations
11.
Szegedy, Christian, Wei Liu, Yangqing Jia, et al.. (2015). Going deeper with convolutions. 1–9.30609 indexed citations breakdown →
12.
Sermanet, Pierre, David Eigen, Xiang Zhang, et al.. (2014). Overfeat: Integrated recognition, localization and detection using convolutional networks. 2nd International Conference on Learning Representations, ICLR 2014. International Conference on Learning Representations.16 indexed citations
13.
Sermanet, Pierre & Yann LeCun. (2011). Traffic sign recognition with multi-scale Convolutional Networks. 2809–2813.526 indexed citations breakdown →
Hadsell, Raia, Alper Nabi Erkan, Pierre Sermanet, et al.. (2007). A multi-range vision strategy for autonomous offroad navigation. 457–463.12 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.