Aruni RoyChowdhury

3.2k total citations · 1 hit paper
6 papers, 1.6k citations indexed

About

Aruni RoyChowdhury is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Aruni RoyChowdhury has authored 6 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 3 papers in Artificial Intelligence and 1 paper in Computational Mechanics. Recurrent topics in Aruni RoyChowdhury's work include Advanced Image and Video Retrieval Techniques (3 papers), Advanced Neural Network Applications (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). Aruni RoyChowdhury is often cited by papers focused on Advanced Image and Video Retrieval Techniques (3 papers), Advanced Neural Network Applications (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). Aruni RoyChowdhury collaborates with scholars based in United States and Cyprus. Aruni RoyChowdhury's co-authors include Subhransu Maji, Tsung‐Yu Lin, Erik Learned-Miller, Rakesh R. Menon, Evangelos Kalogerakis, Matheus Gadelha, R. Manmatha, Yuting Zhang, Ankan Bansal and Vijay Mahadevan and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Computer Graphics Forum and arXiv (Cornell University).

In The Last Decade

Aruni RoyChowdhury

6 papers receiving 1.6k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

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.

Bilinear CNN Models for Fine-Grained Visual Recognition

2015 1.3k citations Tsung‐Yu Lin, Aruni RoyChowdhury et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Aruni RoyChowdhury United States	6	1.2k	640	208	120	102	6	1.6k
Tsung‐Yu Lin United States	9	1.3k 1.1×	679 1.1×	218 1.0×	130 1.1×	102 1.0×	13	2.0k
Mircea Cimpoi France	5	1.5k 1.2×	776 1.2×	214 1.0×	107 0.9×	141 1.4×	6	2.0k
Zihang Jiang China	11	1.2k 1.0×	570 0.9×	263 1.3×	156 1.3×	132 1.3×	23	1.9k
Heliang Zheng China	8	1.6k 1.3×	975 1.5×	240 1.2×	144 1.2×	87 0.9×	13	2.1k
Chun-Fu Richard Chen United States	6	797 0.7×	395 0.6×	199 1.0×	153 1.3×	104 1.0×	12	1.4k
Xiaoyi Dong China	11	1.0k 0.8×	504 0.8×	252 1.2×	105 0.9×	136 1.3×	26	1.6k
Yehui Tang China	15	862 0.7×	491 0.8×	199 1.0×	100 0.8×	101 1.0×	19	1.4k
Ronghang Hu United States	13	1.5k 1.2×	1.1k 1.8×	106 0.5×	105 0.9×	72 0.7×	17	2.2k
Yujun Shi China	12	1.1k 0.9×	616 1.0×	249 1.2×	155 1.3×	137 1.3×	20	2.0k
Diane Larlus France	17	1.9k 1.6×	516 0.8×	231 1.1×	76 0.6×	247 2.4×	32	2.3k

Countries citing papers authored by Aruni RoyChowdhury

Since Specialization

Citations

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

Fields of papers citing papers by Aruni RoyChowdhury

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aruni RoyChowdhury

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

All Works

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6 of 6 papers shown

1.

Liao, Haofu, Aruni RoyChowdhury, Ankan Bansal, et al.. (2023). DocTr: Document Transformer for Structured Information Extraction in Documents. 19527–19537. 5 indexed citations

2.

RoyChowdhury, Aruni, Matheus Gadelha, Liangliang Cao, et al.. (2022). PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation. Computer Graphics Forum. 41(5). 39–50. 10 indexed citations

3.

RoyChowdhury, Aruni, et al.. (2018). The Best of Both Worlds: Combining CNNs and Geometric Constraints for Hierarchical Motion Segmentation. 508–517. 28 indexed citations

4.

Lin, Tsung‐Yu, Aruni RoyChowdhury, & Subhransu Maji. (2017). Bilinear Convolutional Neural Networks for Fine-Grained Visual Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40(6). 1309–1322. 245 indexed citations

5.

RoyChowdhury, Aruni, Tsung‐Yu Lin, Subhransu Maji, & Erik Learned-Miller. (2015). Face Identification with Bilinear CNNs.. arXiv (Cornell University). 12 indexed citations

6.

Lin, Tsung‐Yu, Aruni RoyChowdhury, & Subhransu Maji. (2015). Bilinear CNN Models for Fine-Grained Visual Recognition. 1449–1457. 1334 indexed citations breakdown →

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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