Ivan Laptev A
Impact in
- Computer Vision and Pattern Recognition top 0.01%
- Human Pose and Action Recognition
- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
- Video Surveillance and Tracking Methods
- Video Analysis and Summarization
- Advanced Image and Video Retrieval Techniques
- Context-Aware Activity Recognition Systems
- Human-Computer Interaction top 0.1%
- Hand Gesture Recognition Systems
Papers in
-
- Human Pose and Action Recognition 56
- Multimodal Machine Learning Applications 28
- Advanced Image and Video Retrieval Techniques 20
- Advanced Neural Network Applications 19
- Video Surveillance and Tracking Methods 15
- Video Analysis and Summarization 11
-
- Robot Manipulation and Learning 16
- Co-authors
- Cordelia Schmid (47 shared papers)Barbara Caputo (3 shared papers)Christian Schüldt (3 shared papers)Josef Šivic (48 shared papers)Marcin Marszałek (4 shared papers)Maxime Oquab (5 shared papers)Léon Bottou (4 shared papers)Gül Varol (7 shared papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (6 papers)International Journal of Computer Vision (5 papers)Computer Vision and Image Understanding (3 papers)Lecture notes in computer science (22 papers)Image and Vision Computing (2 papers)
- Partner nations
- FranceCzechiaUnited Kingdom
In The Last Decade
Ivan Laptev A
149 papers receiving 28.1k citations
Ivan Laptev A's Hit Papers
Peers
Comparison fields: 5 of 155
- Computer Vision and Pattern Recognition 22.8k
- Human-Computer Interaction 1.3k
- Computational Mathematics 58
- Artificial Intelligence 3k
- Media Technology 551
Countries citing papers authored by Ivan Laptev A
This map shows the geographic impact of Ivan Laptev A'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 A 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 A more than expected).
Fields of papers citing papers by Ivan Laptev A
This network shows the impact of papers produced by Ivan Laptev A. 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 A. The network helps show where Ivan Laptev A may publish in the future.
Co-authors
The 25 scholars most cited alongside Ivan Laptev A, 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 155 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Learning realistic human actions from movies Hit paper breakdown → | 2008 | 2855 |
| 2 | Recognizing human actions: a local SVM approach Hit paper breakdown → | 2004 | 2554 |
| 3 | Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks Hit paper breakdown → | 2014 | 2412 |
| 4 | Recognizing human actions: A local SVM approach Hit paper breakdown → | 2004 | 2411 |
| 5 | On Space-Time Interest Points Hit paper breakdown → | 2005 | 2144 |
| 6 | Segmenter: Transformer for Semantic Segmentation Hit paper breakdown → | 2021 | 1595 |
| 7 | Evaluation of local spatio-temporal features for action recognition Hit paper breakdown → | 2009 | 1068 |
| 8 | Actions in context Hit paper breakdown → | 2009 | 967 |
| 9 | Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding Hit paper breakdown → | 2016 | 771 |
| 10 | Long-Term Temporal Convolutions for Action Recognition Hit paper breakdown → | 2017 | 746 |
| 11 | Learning from Synthetic Humans Hit paper breakdown → | 2017 | 701 |
| 12 | Is object localization for free? - Weakly-supervised learning with convolutional neural networks Hit paper breakdown → | 2015 | 647 |
| 13 | P-CNN: Pose-Based CNN Features for Action Recognition Hit paper breakdown → | 2015 | 498 |
| 14 | End-to-End Learning of Visual Representations From Uncurated Instructional Videos Hit paper breakdown → | 2020 | 438 |
| 15 | The THUMOS challenge on action recognition for videos “in the wild” Hit paper breakdown → | 2016 | 432 |
| 16 | Learning Joint Reconstruction of Hands and Manipulated Objects Hit paper breakdown → | 2019 | 425 |
| 17 | 2007 | 320 | |
| 18 | 2010 | 314 | |
| 19 | BodyNet: Volumetric Inference of 3D Human Body Shapes Hit paper breakdown → | 2018 | 304 |
| 20 | 2011 | 255 |
About Ivan Laptev A
Ivan Laptev A is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering, Artificial Intelligence, Ocean Engineering and Computational Mechanics, having authored 155 papers that have together received 28.9k indexed citations. Recurring topics across this work include Human Pose and Action Recognition (56 papers), Multimodal Machine Learning Applications (28 papers), Advanced Image and Video Retrieval Techniques (20 papers), Advanced Neural Network Applications (19 papers), Robot Manipulation and Learning (16 papers), Video Surveillance and Tracking Methods (15 papers), Reinforcement Learning in Robotics (11 papers) and Video Analysis and Summarization (11 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (22.8k citations), Human-Computer Interaction (1.3k citations), Computational Mathematics (58 citations), Artificial Intelligence (3k citations) and Media Technology (551 citations). Ivan Laptev A has collaborated with scholars based in France, Czechia and United Kingdom. Frequent co-authors include Cordelia Schmid, Barbara Caputo, Christian Schüldt, Josef Šivic, Marcin Marszałek, Maxime Oquab, Léon Bottou, Gül Varol, Robin Strudel and Ricardo Alexandrino Garcia. 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.