Snehasis Mukherjee

1.6k total citations · 1 hit paper
43 papers, 900 citations indexed

About

Snehasis Mukherjee is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Human-Computer Interaction. According to data from OpenAlex, Snehasis Mukherjee has authored 43 papers receiving a total of 900 indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Computer Vision and Pattern Recognition, 18 papers in Artificial Intelligence and 4 papers in Human-Computer Interaction. Recurrent topics in Snehasis Mukherjee's work include Human Pose and Action Recognition (14 papers), Advanced Neural Network Applications (10 papers) and Video Surveillance and Tracking Methods (10 papers). Snehasis Mukherjee is often cited by papers focused on Human Pose and Action Recognition (14 papers), Advanced Neural Network Applications (10 papers) and Video Surveillance and Tracking Methods (10 papers). Snehasis Mukherjee collaborates with scholars based in India and United States. Snehasis Mukherjee's co-authors include Shiv Ram Dubey, S. H. Shabbeer Basha, P. Viswanath, Soumendu Chakraborty, Swalpa Kumar Roy, B.B. Chaudhuri, Satish Kumar Singh, Dipti Prasad Mukherjee, Sujoy Biswas and Suvadip Mukherjee and has published in prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems, Neurocomputing and Neural Networks.

In The Last Decade

Snehasis Mukherjee

40 papers receiving 860 citations

Hit Papers

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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.

Impact of fully connected layers on performance of convolutional neural networks for image classification

2019 353 citations S. H. Shabbeer Basha, Shiv Ram Dubey et al. Neurocomputing profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Snehasis Mukherjee India	12	450	283	122	91	76	43	900
Chirag Patel India	11	370 0.8×	255 0.9×	81 0.7×	120 1.3×	74 1.0×	37	961
Diganta Misra India	4	552 1.2×	187 0.7×	128 1.0×	77 0.8×	72 0.9×	8	958
Bilel Benjdira Saudi Arabia	16	476 1.1×	190 0.7×	161 1.3×	72 0.8×	64 0.8×	30	1.1k
Xin Tan China	21	772 1.7×	318 1.1×	127 1.0×	52 0.6×	62 0.8×	84	1.3k
C. H. Chen United States	5	389 0.9×	240 0.8×	101 0.8×	59 0.6×	46 0.6×	12	855
Mohammed El Hassouni Morocco	16	374 0.8×	141 0.5×	102 0.8×	54 0.6×	72 0.9×	100	846
Chun-Fu Richard Chen United States	6	797 1.8×	395 1.4×	199 1.6×	153 1.7×	115 1.5×	12	1.4k
Zhongchao Shi China	14	535 1.2×	254 0.9×	94 0.8×	104 1.1×	57 0.8×	61	1.0k

Countries citing papers authored by Snehasis Mukherjee

Since Specialization

Citations

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

Fields of papers citing papers by Snehasis Mukherjee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Snehasis Mukherjee

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

All Works

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20 of 20 papers shown

Mukherjee, Snehasis, et al.. (2025). Scale-invariant object detection by adaptive convolution with unified global-local context. Evolving Systems. 16(4).

Mukherjee, Snehasis, et al.. (2023). DFW-PP: dynamic feature weighting-based popularity prediction for social media content. The Journal of Supercomputing. 80(4). 5708–5730. 2 indexed citations

Mukherjee, Snehasis, et al.. (2023). STTGC-Net: Spatial-Temporal Transformer with Graph Convolution for Skeleton-Based Action Recognition. 1–10. 1 indexed citations

Mukherjee, Snehasis, et al.. (2023). Auto CNN classifier based on knowledge transferred from self-supervised model. Applied Intelligence. 53(19). 22086–22104. 4 indexed citations

Basha, S. H. Shabbeer, P. Viswanath, & Snehasis Mukherjee. (2022). An information-rich sampling technique over spatio-temporal CNN for classification of human actions in videos. Multimedia Tools and Applications. 81(28). 40431–40449. 9 indexed citations

Mukherjee, Snehasis, et al.. (2022). First-person Activity Recognition by Modelling Subject - Action Relevance. 2022 International Joint Conference on Neural Networks (IJCNN). 1–8.

Mukherjee, Snehasis, et al.. (2020). Measuring photography aesthetics with deep CNNs. IET Image Processing. 14(8). 1561–1570. 18 indexed citations

Mukherjee, Snehasis, et al.. (2020). Single image dehazing by approximating and eliminating the additional airlight component. Neurocomputing. 400. 294–308. 23 indexed citations

Basha, S. H. Shabbeer, et al.. (2020). AutoTune: Automatically Tuning Convolutional Neural Networks for Improved Transfer Learning. Neural Networks. 133. 112–122. 37 indexed citations

10.

Dubey, Shiv Ram, Soumendu Chakraborty, Swalpa Kumar Roy, et al.. (2019). diffGrad: An Optimization Method for Convolutional Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. 31(11). 4500–4511. 185 indexed citations

11.

Mukherjee, Snehasis, et al.. (2018). SceneEDNet: A Deep Learning Approach for Scene Flow Estimation. arXiv (Cornell University). 394–399. 2 indexed citations

12.

Mukherjee, Snehasis, et al.. (2018). Video Dehazing using LMNN with respect to Augmented MRF. 1–9. 1 indexed citations

13.

Basha, S. H. Shabbeer, et al.. (2018). RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification. arXiv (Cornell University). 1222–1227. 38 indexed citations

14.

Chaudhury, Santanu, Anoop Namboodiri, Srirangaraj Setlur, et al.. (2017). Computer Vision, Graphics, and Image Processing. Lecture notes in computer science. 13 indexed citations

15.

Mukherjee, Snehasis, et al.. (2017). Human action and event recognition using a novel descriptor based on improved dense trajectories. Multimedia Tools and Applications. 77(11). 13661–13678. 2 indexed citations

16.

Mukherjee, Snehasis, et al.. (2016). Recognizing facial expressions using novel motion based features. 1–8. 3 indexed citations

17.

Mukherjee, Snehasis, et al.. (2016). Event recognition in egocentric videos using a novel trajectory based feature. 1–8. 4 indexed citations

18.

Mukherjee, Snehasis, et al.. (2011). A design-of-experiment based statistical technique for detection of key-frames. Multimedia Tools and Applications. 62(3). 847–877. 4 indexed citations

19.

Mukherjee, Snehasis & Dipti Prasad Mukherjee. (2009). Tracking Multiple Circular Objects in Video Using Helmholtz Principle. 34. 290–293. 1 indexed citations

20.

Mukherjee, Snehasis & Dipti Prasad Mukherjee. (2008). Segmentation of Circular and Rectangular Shapes in an Image Using Helmholtz Principle. 34. 657–664. 2 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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