Terrance DeVries

3.6k total citations
3 papers, 113 citations indexed

About

Terrance DeVries is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Experimental and Cognitive Psychology. According to data from OpenAlex, Terrance DeVries has authored 3 papers receiving a total of 113 indexed citations (citations by other indexed papers that have themselves been cited), including 1 paper in Computer Vision and Pattern Recognition, 1 paper in Artificial Intelligence and 1 paper in Experimental and Cognitive Psychology. Recurrent topics in Terrance DeVries's work include Emotion and Mood Recognition (1 paper), Domain Adaptation and Few-Shot Learning (1 paper) and Machine Learning and Data Classification (1 paper). Terrance DeVries is often cited by papers focused on Emotion and Mood Recognition (1 paper), Domain Adaptation and Few-Shot Learning (1 paper) and Machine Learning and Data Classification (1 paper). Terrance DeVries collaborates with scholars based in Israel, Canada and India. Terrance DeVries's co-authors include Graham W. Taylor, Changhan Wang, Laurens van der Maaten and Ishan Misra and has published in prestigious journals such as arXiv (Cornell University) and Computer Vision and Pattern Recognition.

In The Last Decade

Terrance DeVries

3 papers receiving 104 citations

Peers

Terrance DeVries

CVPR

ECP

AI

SP

MT

Carles Ventura Spain

Xutao Lv United States

Cristian Canton Ferrer United States

Grégoire Lefebvre France

Trisha Mittal United States

Abdelkrim Ouafi Algeria

Seung Ho Lee South Korea

Badri N. Patro India

Alice Caplier France

Carles Ventura Spain

Terrance DeVries

168 ×2.0

CVPR

22 ×0.5

ECP

28 ×1.0

AI

9 ×0.6

SP

4 ×0.6

MT

Citations per year, relative to Terrance DeVries Terrance DeVries (= 1×) peers Carles Ventura

Countries citing papers authored by Terrance DeVries

Since Specialization

Citations

This map shows the geographic impact of Terrance DeVries'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 Terrance DeVries with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Terrance DeVries more than expected).

Fields of papers citing papers by Terrance DeVries

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Terrance DeVries. 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 Terrance DeVries. The network helps show where Terrance DeVries may publish in the future.

Co-authorship network of co-authors of Terrance DeVries

This figure shows the co-authorship network connecting the top 25 collaborators of Terrance DeVries. A scholar is included among the top collaborators of Terrance DeVries 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 Terrance DeVries. Terrance DeVries is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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3 of 3 papers shown

1.

DeVries, Terrance, Ishan Misra, Changhan Wang, & Laurens van der Maaten. (2019). Does Object Recognition Work for Everyone. Computer Vision and Pattern Recognition. 52–59. 12 indexed citations

2.

DeVries, Terrance & Graham W. Taylor. (2017). Dataset Augmentation in Feature Space. arXiv (Cornell University). 36 indexed citations

3.

DeVries, Terrance, et al.. (2014). Multi-task Learning of Facial Landmarks and Expression. 98–103. 65 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.

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