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.
Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking
2023407 citationsXinshuo Weng, Rawal Khirodkar et al.profile →
AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting
2021331 citationsYe Yuan, Xinshuo Weng et al.2021 IEEE/CVF International Conference on Computer Vision (ICCV)profile →
3D Multi-Object Tracking: A Baseline and New Evaluation Metrics
2020311 citationsXinshuo Weng, Jianren Wang et al.profile →
Rethinking Transformer-based Set Prediction for Object Detection
2021255 citationsShengcao Cao, Kris Kitani et al.2021 IEEE/CVF International Conference on Computer Vision (ICCV)profile →
No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency
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).
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.
Yuan, Ye & Kris Kitani. (2020). Diverse Trajectory Forecasting with Determinantal Point Processes. arXiv (Cornell University).17 indexed citations
6.
Yuan, Ye & Kris Kitani. (2020). Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis. Neural Information Processing Systems. 33. 21763–21774.4 indexed citations
Weng, Xinshuo & Kris Kitani. (2019). Learning Spatio-Temporal Features with Two-Stream Deep 3D CNNs for Lipreading.. British Machine Vision Conference. 269.8 indexed citations
12.
Sharma, Mohit, et al.. (2018). Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information.. International Conference on Learning Representations.6 indexed citations
Weng, Xinshuo, et al.. (2018). GroundNet: Segmentation-Aware Monocular Ground Plane Estimation with Geometric Consistency.. arXiv (Cornell University).2 indexed citations
15.
Chen, Jia, Shizhe Chen, Qin Jin, et al.. (2018). Informedia @ TRECVID 2018: Ad-hoc Video Search, Video to Text Description, Activities in Extended video.. TRECVID.2 indexed citations
Rhinehart, Nicholas, et al.. (2017). N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning.. International Conference on Learning Representations.14 indexed citations
18.
Rhinehart, Nicholas & Kris Kitani. (2016). First-Person Forecasting with Online Inverse Reinforcement Learning. arXiv (Cornell University).1 indexed citations
19.
Rhinehart, Nicholas & Kris Kitani. (2016). Online Semantic Activity Forecasting with DARKO.. arXiv (Cornell University).4 indexed citations
20.
Kitani, Kris, et al.. (2014). Automating Stroke Rehabilitation for Home-Based Therapy. National Conference on Artificial Intelligence.1 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.