Kris Kitani
About
In The Last Decade
Kris Kitani
161 papers receiving 6.2k citations
Hit Papers
Peers
Comparison fields: 5 of 160
- Computer Vision and Pattern Recognition 4.0k
- Artificial Intelligence 1.2k
- Cognitive Neuroscience 1.2k
- Human-Computer Interaction 1.1k
- Automotive Engineering 905
Countries citing papers authored by Kris Kitani
This map shows the geographic impact of Kris Kitani'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 Kris Kitani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kris Kitani more than expected).
Fields of papers citing papers by Kris Kitani
This network shows the impact of papers produced by Kris Kitani. 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 Kris Kitani. The network helps show where Kris Kitani may publish in the future.
Co-authorship network of co-authors of Kris Kitani
This figure shows the co-authorship network connecting the top 25 collaborators of Kris Kitani. A scholar is included among the top collaborators of Kris Kitani 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 Kris Kitani. Kris Kitani is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 3 | |
| 3 | Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking breakdown → | 407 |
| 4 | 15 | |
| 5 | 184 | |
| 6 | 18 | |
| 7 | 152 | |
| 8 | Diverse Trajectory Forecasting with Determinantal Point Processes | 17 |
| 9 | Sequential Forecasting of 100,000 Points | 4 |
| 10 | 23 | |
| 11 | A Baseline for 3D Multi-Object Tracking | 40 |
| 12 | Learning Spatio-Temporal Features with Two-Stream Deep 3D CNNs for Lipreading. | 8 |
| 13 | Learnable Embedding Space for Efficient Neural Architecture Compression | 1 |
| 14 | Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information. | 6 |
| 15 | GroundNet: Segmentation-Aware Monocular Ground Plane Estimation with Geometric Consistency. | 2 |
| 16 | Informedia @ TRECVID 2018: Ad-hoc Video Search, Video to Text Description, Activities in Extended video. | 2 |
| 17 | Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection. | 47 |
| 18 | N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning. | 14 |
| 19 | Online Semantic Activity Forecasting with DARKO. | 4 |
| 20 | Automating Stroke Rehabilitation for Home-Based Therapy | 1 |
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.