Ivan Laptev
Impact in
- Computer Vision and Pattern Recognition top 0.02%
- Human Pose and Action Recognition
- Video Surveillance and Tracking Methods
- Multimodal Machine Learning Applications
- Video Analysis and Summarization
- Advanced Image and Video Retrieval Techniques
- Human-Computer Interaction top 0.05%
- Hand Gesture Recognition Systems
Papers in
-
- Human Pose and Action Recognition 56
- Multimodal Machine Learning Applications 33
- Video Analysis and Summarization 22
- Video Surveillance and Tracking Methods 21
- Advanced Image and Video Retrieval Techniques 20
- Advanced Vision and Imaging 19
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- Anomaly Detection Techniques and Applications 22
- Domain Adaptation and Few-Shot Learning 11
- Co-authors
- Cordelia Schmid (27 shared papers)Josef Šivic (32 shared papers)Marcin Marszałek (4 shared papers)Barbara Caputo (2 shared papers)Christian Schüldt (2 shared papers)Maxime Oquab (3 shared papers)Léon Bottou (2 shared papers)Gül Varol (5 shared papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (6 papers)International Journal of Computer Vision (4 papers)Computer Vision and Image Understanding (4 papers)Lecture notes in computer science (19 papers)Image and Vision Computing (2 papers)
- Partner nations
- FranceUnited KingdomCzechia
In The Last Decade
Ivan Laptev
109 papers receiving 19.0k citations
Ivan Laptev's Hit Papers
Peers
Comparison fields: 5 of 181
- Computer Vision and Pattern Recognition 17.2k
- Human-Computer Interaction 2.4k
- Artificial Intelligence 8.3k
- Biomedical Engineering 3.2k
- Signal Processing 713
Countries citing papers authored by Ivan Laptev
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).
Fields of papers citing papers by Ivan Laptev
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-authors
The 25 scholars most cited alongside Ivan Laptev, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 113 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Learning realistic human actions from movies Hit paper breakdown → | 2008 | 2748 |
| 2 | Recognizing human actions: a local SVM approach Hit paper breakdown → | 2004 | 2453 |
| 3 | Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks Hit paper breakdown → | 2014 | 2278 |
| 4 | On Space-Time Interest Points Hit paper breakdown → | 2005 | 2059 |
| 5 | Evaluation of local spatio-temporal features for action recognition Hit paper breakdown → | 2009 | 1027 |
| 6 | Actions in context Hit paper breakdown → | 2009 | 918 |
| 7 | Long-Term Temporal Convolutions for Action Recognition Hit paper breakdown → | 2017 | 705 |
| 8 | Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding Hit paper breakdown → | 2016 | 652 |
| 9 | HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million\n Narrated Video Clips Hit paper breakdown → | 2019 | 610 |
| 10 | End-to-End Learning of Visual Representations From Uncurated Instructional Videos Hit paper breakdown → | 2020 | 403 |
| 11 | The THUMOS challenge on action recognition for videos “in the wild” Hit paper breakdown → | 2016 | 380 |
| 12 | 2010 | 308 | |
| 13 | 2007 | 305 | |
| 14 | BodyNet: Volumetric Inference of 3D Human Body Shapes Hit paper breakdown → | 2018 | 257 |
| 15 | 2003 | 251 | |
| 16 | 2011 | 237 | |
| 17 | 2016 | 197 | |
| 18 | 2009 | 191 | |
| 19 | 2014 | 175 | |
| 20 | 2008 | 168 |
About Ivan Laptev
Ivan Laptev is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering, Human-Computer Interaction and Aerospace Engineering, having authored 113 papers that have together received 19.7k indexed citations. Recurring topics across this work include Human Pose and Action Recognition (56 papers), Multimodal Machine Learning Applications (33 papers), Anomaly Detection Techniques and Applications (22 papers), Video Analysis and Summarization (22 papers), Video Surveillance and Tracking Methods (21 papers), Advanced Image and Video Retrieval Techniques (20 papers), Advanced Vision and Imaging (19 papers) and Domain Adaptation and Few-Shot Learning (11 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (17.2k citations), Human-Computer Interaction (2.4k citations), Artificial Intelligence (8.3k citations), Biomedical Engineering (3.2k citations) and Signal Processing (713 citations). Ivan Laptev has collaborated with scholars based in France, United Kingdom and Czechia. Frequent co-authors include Cordelia Schmid, Josef Šivic, Marcin Marszałek, Barbara Caputo, Christian Schüldt, Maxime Oquab, Léon Bottou, Gül Varol, Tony Lindeberg and Muhammad Muneeb Ullah. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision, Computer Vision and Image Understanding, Lecture notes in computer science and Image and Vision Computing.
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.