Catherine Wah
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Media Technology top 10%
- Computer Science Applications top 10%
- Computational Mechanics
- Co-authors
- Serge BelongieSteve BransonPietro PeronaAnurag BhardwajDi WeiNeel SundaresanRobinson PiramuthuGrant Van Horn
- Topics
- Advanced Image and Video Retrieval Techniques (5 papers)Domain Adaptation and Few-Shot Learning (5 papers)Image Retrieval and Classification Techniques (2 papers)
- Journals
- International Journal of Computer VisionCaltechAUTHORS (California Institute of Technology)
- Partner nations
- United States
In The Last Decade
Catherine Wah
9 papers receiving 697 citations
Hit Papers
Peers
Comparison fields: 5 of 87
- Computer Vision and Pattern Recognition 491
- Artificial Intelligence 367
- Media Technology 38
- Computer Science Applications 37
- Computational Mechanics 32
Countries citing papers authored by Catherine Wah
This map shows the geographic impact of Catherine Wah'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 Catherine Wah with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Catherine Wah more than expected).
Fields of papers citing papers by Catherine Wah
This network shows the impact of papers produced by Catherine Wah. 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 Catherine Wah. The network helps show where Catherine Wah may publish in the future.
Co-authorship network of co-authors of Catherine Wah
This figure shows the co-authorship network connecting the top 25 collaborators of Catherine Wah. A scholar is included among the top collaborators of Catherine Wah 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 Catherine Wah. Catherine Wah is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Caltech-UCSD Birds-200-2011 Datasetbreakdown → | 369 |
| 2 | 6 | |
| 3 | 50 | |
| 4 | 43 | |
| 5 | 14 | |
| 6 | 89 | |
| 7 | 112 | |
| 8 | Crowdsourcing and Its Applications in Computer Vision | 6 |
| 9 | Caltech-UCSD Birds 200 | 30 |
About Catherine Wah
Catherine Wah is a scholar working on Developmental Biology, Computer Vision and Pattern Recognition and Ecological Modeling, having authored 9 papers that have together received 719 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (5 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Image Retrieval and Classification Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (491 citations), Artificial Intelligence (367 citations) and Developmental Biology (16 citations). Catherine Wah has collaborated with scholars based in United States. Frequent co-authors include Serge Belongie, Steve Branson, Pietro Perona, Anurag Bhardwaj, Di Wei, Neel Sundaresan, Robinson Piramuthu, Grant Van Horn, Subhransu Maji and Florian Schroff. Their work appears in journals such as International Journal of Computer Vision and CaltechAUTHORS (California Institute of Technology).
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.