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.
Learning realistic human actions from movies
20082.4k citationsIvan Laptev, Marcin Marszałek et al.HAL (Le Centre pour la Communication Scientifique Directe)profile →
This map shows the geographic impact of Ivan Laptev'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 Ivan Laptev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ivan Laptev more than expected).
This network shows the impact of papers produced by Ivan Laptev. 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 Ivan Laptev. The network helps show where Ivan Laptev may publish in the future.
Co-authorship network of co-authors of Ivan Laptev
This figure shows the co-authorship network connecting the top 25 collaborators of Ivan Laptev.
A scholar is included among the top collaborators of Ivan Laptev 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 Ivan Laptev. Ivan Laptev is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yang, Antoine, Antoine Miech, Josef Šivic, Ivan Laptev, & Cordelia Schmid. (2022). TubeDETR: Spatio-Temporal Video Grounding with Transformers. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 16421–16432.61 indexed citations
4.
Yang, Antoine, Antoine Miech, Josef Šivic, Ivan Laptev, & Cordelia Schmid. (2022). Learning to Answer Visual Questions From Web Videos. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(5). 3202–3218.19 indexed citations
Laptev, Ivan, et al.. (2010). INRIA-WILLOW at TRECVid 2010: Surveillance Event Detection. TRECVID.1 indexed citations
15.
Marszałek, Marcin, Ivan Laptev, & Cordelia Schmid. (2009). Actions in context. 2009 IEEE Conference on Computer Vision and Pattern Recognition. 2929–2936.779 indexed citations breakdown →
16.
Laptev, Ivan, et al.. (2008). Learning realistic human actions from movies. HAL (Le Centre pour la Communication Scientifique Directe). 1–8.2370 indexed citations breakdown →
17.
Laptev, Ivan. (2005). On Space-Time Interest Points. International Journal of Computer Vision. 64(2-3). 107–123.1753 indexed citations breakdown →
18.
Mayer, Helmut, Ivan Laptev, Albert Baumgartner, & Carsten Steger. (2002). AUTOMATIC ROAD EXTRACTION BASED ON MULTI-SCALE MODELING, CONTEXT, AND SNAKES.35 indexed citations
19.
Laptev, Ivan & Tony Lindeberg. (2002). Velocity-adapted spatio-temporal receptive fields for direct recognition of activities. KTH Publication Database DiVA (KTH Royal Institute of Technology). 61–66.6 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.