Christopher Xie
- Computer Vision and Pattern Recognition top 5%
- Control and Systems Engineering
- Artificial Intelligence
- Aerospace Engineering
- Computational Mechanics
- Co-authors
- Dieter FoxXiang YuArsalan MousavianSachin PatilPieter AbbeelJur van den BergRicardo Martin-BruallaKeunhong Park
- Topics
- Human Pose and Action Recognition (3 papers)Advanced Neural Network Applications (3 papers)Robot Manipulation and Learning (2 papers)
- Cited by
- Computer Vision and Pattern RecognitionComputer Graphics and Computer-Aided DesignControl and Systems Engineering
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Christopher Xie
7 papers receiving 179 citations
Peers
Comparison fields: 5 of 36
- Computer Vision and Pattern Recognition 147
- Control and Systems Engineering 60
- Artificial Intelligence 48
- Aerospace Engineering 47
- Computational Mechanics 16
Countries citing papers authored by Christopher Xie
This map shows the geographic impact of Christopher Xie'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 Christopher Xie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christopher Xie more than expected).
Fields of papers citing papers by Christopher Xie
This network shows the impact of papers produced by Christopher Xie. 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 Christopher Xie. The network helps show where Christopher Xie may publish in the future.
Co-authorship network of co-authors of Christopher Xie
This figure shows the co-authorship network connecting the top 25 collaborators of Christopher Xie. A scholar is included among the top collaborators of Christopher Xie 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 Christopher Xie. Christopher Xie is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 26 | |
| 2 | 81 | |
| 3 | Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation | 2 |
| 4 | 26 | |
| 5 | The Best of Both Modes: Separately Leveraging RGB and Depth for Unseen Object Instance Segmentation. | 10 |
| 6 | 10 | |
| 7 | 32 |
About Christopher Xie
Christopher Xie is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Control and Systems Engineering, having authored 7 papers that have together received 187 indexed citations. Recurring topics across this work include Human Pose and Action Recognition (3 papers), Advanced Neural Network Applications (3 papers) and Robot Manipulation and Learning (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (147 citations), Computer Graphics and Computer-Aided Design (15 citations) and Control and Systems Engineering (60 citations). Christopher Xie has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Dieter Fox, Xiang Yu, Arsalan Mousavian, Sachin Patil, Pieter Abbeel, Jur van den Berg, Ricardo Martin-Brualla, Keunhong Park, Zaïd Harchaoui and Juan Lavista Ferres. Their work appears in journals such as IEEE Transactions on Robotics and IEEE Signal Processing Letters.
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.