Priya Goyal

41.9k citations
9 papers · 6.5k · 1 hit paper · h-index 5

Impact in

Papers in

Priya Goyal

8 papers receiving 6.3k citations

Priya Goyal's Hit Papers

Focal Loss for Dense Object Detection 2018 · 6.4k citations
6.4k0+2+5Years since publication2.0k4.0k6.0k

Peers

Priya Goyal
Comparison fields: 5 of 182
  • Computer Vision and Pattern Recognition 3.2k
  • Media Technology 683
  • Industrial and Manufacturing Engineering 508
  • Artificial Intelligence 1.6k
  • Health Informatics 39
Replace Zhuang Liu with:
Zhuang Liu China
Hanzi Mao United States
Tete Xiao United States
Gang Sun China
Samuel Albanie United Kingdom
Paul Fieguth Canada
Lingxi Xie China
Tsung-Yi Lin United States
Li Shen China
Priya Goyal relative to Zhuang Liu China Zhuang Liu's profile →
Citations per field
00.5×1.7×
Zhuang Liu · 1×
Citations per year

Countries citing papers authored by Priya Goyal

Since Specialization
Citations

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

Fields of papers citing papers by Priya Goyal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 21 scholars most cited alongside Priya Goyal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Priya Goyal Line = papers co-authored together Priya Goyal links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1
Focal Loss for Dense Object Detection
Hit paper breakdown →
20186433
2 202237
3 201929
4 202310
5 20228
6 20163
7
A Study on 5G Evolution and Revolution
20153
8 20252
9 20250

About Priya Goyal

Priya Goyal is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture and Surgery, having authored 9 papers that have together received 6.5k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (3 papers), Parallel Computing and Optimization Techniques (2 papers), Advanced Neural Network Applications (2 papers), Explainable Artificial Intelligence (XAI) (1 paper), Multimodal Machine Learning Applications (1 paper), Healthcare Technology and Patient Monitoring (1 paper), Advanced MIMO Systems Optimization (1 paper) and Cooperative Communication and Network Coding (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (3.2k citations), Media Technology (683 citations), Industrial and Manufacturing Engineering (508 citations), Artificial Intelligence (1.6k citations) and Health Informatics (39 citations). Priya Goyal has collaborated with scholars based in United States, India and Israel. Frequent co-authors include Ross Girshick, Kaiming He, Tsung-Yi Lin, Piotr Dollár, Matthijs Douze, Sven Verdoolaege, Oleksandr Zinenko, Albert Cohen, Zachary DeVito and William S. Moses. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, ACM Transactions on Architecture and Code Optimization, eScholarship (California Digital Library), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and American Heart Journal Plus Cardiology Research and Practice.

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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