Samik Banerjee

783 total citations
11 papers, 86 citations indexed

About

Samik Banerjee is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Samik Banerjee has authored 11 papers receiving a total of 86 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 3 papers in Signal Processing. Recurrent topics in Samik Banerjee's work include Face recognition and analysis (8 papers), Face and Expression Recognition (5 papers) and Domain Adaptation and Few-Shot Learning (5 papers). Samik Banerjee is often cited by papers focused on Face recognition and analysis (8 papers), Face and Expression Recognition (5 papers) and Domain Adaptation and Few-Shot Learning (5 papers). Samik Banerjee collaborates with scholars based in India, United States and Jordan. Samik Banerjee's co-authors include Sukhendu Das, Mohanasankar Sivaprakasam, Suranjana Samanta, Xu Li, Dingkang Wang, Keerthi Ram, Partha P. Mitra, Yusu Wang, Bing‐Xing Huo and Katherine S. Matho and has published in prestigious journals such as Annals of the Rheumatic Diseases, Neurocomputing and Pattern Recognition Letters.

In The Last Decade

Samik Banerjee

11 papers receiving 80 citations

Peers

Samik Banerjee

CVPR

SP

AI

BE

BIOPH

Xingjian He China

Stefan Gehlen Germany

Grégoire Lefebvre France

Klemen Grm Slovenia

Xutao Lv United States

Sri-Kaushik Pavani Spain

Badri N. Patro India

Wen–Sheng Chu United States

ZhenQiu Zhang China

Yuping Luo United States

Xingjian He China

Samik Banerjee

125 ×2.5

CVPR

8 ×0.3

SP

24 ×1.3

AI

7 ×0.9

BE

3 ×0.5

BIOPH

Citations per year, relative to Samik Banerjee Samik Banerjee (= 1×) peers Xingjian He

Countries citing papers authored by Samik Banerjee

Since Specialization

Citations

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

Fields of papers citing papers by Samik Banerjee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Samik Banerjee

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

All Works

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11 of 11 papers shown

1.

Banerjee, Samik, Dingkang Wang, Xu Li, et al.. (2020). Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder–decoder deep networks. Nature Machine Intelligence. 2(10). 585–594. 21 indexed citations

2.

Banerjee, Samik & Sukhendu Das. (2018). Mutual variation of information on transfer-CNN for face recognition with degraded probe samples. Neurocomputing. 310. 299–315. 28 indexed citations

3.

Banerjee, Samik & Sukhendu Das. (2018). MakeUpMirror: mirroring make‐ups and verifying faces post make‐up. IET Biometrics. 7(6). 598–605. 3 indexed citations

4.

Banerjee, Samik & Sukhendu Das. (2018). LR-GAN for degraded Face Recognition. Pattern Recognition Letters. 116. 246–253. 10 indexed citations

5.

Elmazoğlu, Zübeyir, Samik Banerjee, Berivan Bıtık, et al.. (2018). AB0090 Verbascoside and hydroxytyrosol downregulate stress-related pathways in human osteoarthritic articular chondrocytes. Annals of the Rheumatic Diseases. 77. 1241–1241. 3 indexed citations

6.

Banerjee, Samik, et al.. (2018). Deep Domain Adaptation for Face Recognition using images captured from surveillance cameras. 1–12. 2 indexed citations

7.

Banerjee, Samik, et al.. (2016). Detecting aircrafts from satellite images using saliency and conical pyramid based template representation. Sadhana. 41(10). 1155–1171. 2 indexed citations

8.

Banerjee, Samik & Sukhendu Das. (2016). Soft-Margin Learning for Multiple Feature-Kernel Combinations with Domain Adaptation, for Recognition in Surveillance Face Datasets. 237–242. 5 indexed citations

9.

Banerjee, Samik & Sukhendu Das. (2016). Soft-margin learning for multiple feature-kernel combinations with Domain Adaptation, for recognition in surveillance face datasets. 6 indexed citations

10.

Samanta, Suranjana, Samik Banerjee, & Sukhendu Das. (2015). Unsupervised method of Domain Adaptation on representation of discriminatory regions of the face image for surveillance face datasets. 123–132. 1 indexed citations

11.

Banerjee, Samik, Suranjana Samanta, & Sukhendu Das. (2014). Face Recognition in Surveillance Conditions with Bag-of-words, using Unsupervised Domain Adaptation. 1–8. 5 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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