Abir Das

2.1k total citations · 1 hit paper
22 papers, 988 citations indexed

About

Abir Das is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Abir Das has authored 22 papers receiving a total of 988 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Vision and Pattern Recognition, 12 papers in Artificial Intelligence and 2 papers in Computer Networks and Communications. Recurrent topics in Abir Das's work include Video Surveillance and Tracking Methods (5 papers), Multimodal Machine Learning Applications (5 papers) and Video Analysis and Summarization (4 papers). Abir Das is often cited by papers focused on Video Surveillance and Tracking Methods (5 papers), Multimodal Machine Learning Applications (5 papers) and Video Analysis and Summarization (4 papers). Abir Das collaborates with scholars based in United States, India and Singapore. Abir Das's co-authors include Kate Saenko, Huijuan Xu, Amit K. Roy–Chowdhury, Vasili Ramanishka, Rameswar Panda, Jianming Zhang, Ziyan Wu, Dong Huk Park, Lisa Anne Hendricks and Subhashini Venugopalan and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Scientific Reports and IEEE Access.

In The Last Decade

Abir Das

21 papers receiving 969 citations

Hit Papers

R-C3D: Region Convolutional 3D Network for Temporal Activ... 2017 2026 2020 2023 2017 100 200 300 400
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.

  1. R-C3D: Region Convolutional 3D Network for Temporal Activity Detection
    2017 435 citations Huijuan Xu, Abir Das et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Abir Das United States 11 802 426 96 74 50 22 988
Annan Li China 19 854 1.1× 286 0.7× 237 2.5× 98 1.3× 39 0.8× 39 1.1k
Xitong Yang United States 12 615 0.8× 285 0.7× 59 0.6× 34 0.5× 27 0.5× 29 810
Hefei Ling China 18 972 1.2× 224 0.5× 179 1.9× 108 1.5× 22 0.4× 139 1.1k
Lijie Fan United States 9 369 0.5× 229 0.5× 143 1.5× 41 0.6× 88 1.8× 19 574
Aditi Roy India 13 335 0.4× 107 0.3× 215 2.2× 168 2.3× 48 1.0× 31 564
Donghao Luo China 11 755 0.9× 345 0.8× 122 1.3× 47 0.6× 33 0.7× 33 931
Antonino Furnari Italy 15 671 0.8× 252 0.6× 46 0.5× 36 0.5× 38 0.8× 61 825
Jianshu Li China 20 1.1k 1.4× 298 0.7× 44 0.5× 241 3.3× 28 0.6× 42 1.3k

Countries citing papers authored by Abir Das

Since Specialization
Citations

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

Fields of papers citing papers by Abir Das

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Abir Das

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

All Works

20 of 20 papers shown
1.
Das, Abir, Saurabh Singh, Jaejeung Kim, Tariq Ahamed Ahanger, & Anil Audumbar Pise. (2025). Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models. Scientific Reports. 15(1). 27161–27161.
2.
Das, Abir, et al.. (2024). Goals are Enough: Inducing AdHoc Cooperation Among Unseen Multi-Agent Systems in IMFs. 2. 425–432. 1 indexed citations
3.
Dey, Kaushik, et al.. (2024). Towards Adaptive Networks - Generalized utility functions in Multi-Agent Frameworks. 336–340. 1 indexed citations
4.
Roy, Anurag, et al.. (2024). Convolutional Prompting meets Language Models for Continual Learning. 23616–23626. 5 indexed citations
5.
Panda, Rameswar, et al.. (2023). Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 4199–4208. 13 indexed citations
6.
Roy, Anurag, et al.. (2023). Exemplar-Free Continual Transformer with Convolutions. 5874–5884. 4 indexed citations
7.
Panda, Rameswar, et al.. (2021). Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing. arXiv (Cornell University). 34. 5 indexed citations
8.
Soltani, Mohammad, et al.. (2020). Reinforcement Learning Based Load Balancing for Hybrid LiFi WiFi Networks. IEEE Access. 8. 132273–132284. 42 indexed citations
9.
Sadhu, Anup, et al.. (2020). Quantitative analysis of brain herniation from non-contrast CT images using deep learning. Journal of Neuroscience Methods. 349. 109033–109033. 7 indexed citations
10.
Xu, Huijuan, Abir Das, & Kate Saenko. (2019). Two-Stream Region Convolutional 3D Network for Temporal Activity Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 41(10). 2319–2332. 26 indexed citations
11.
Das, Abir, et al.. (2018). RISE: Randomized Input Sampling for Explanation of Black-box Models. arXiv (Cornell University). 151. 96 indexed citations
12.
Ramanishka, Vasili, Abir Das, Jianming Zhang, & Kate Saenko. (2017). Top-Down Visual Saliency Guided by Captions. 3135–3144. 90 indexed citations
13.
Xu, Huijuan, Abir Das, & Kate Saenko. (2017). R-C3D: Region Convolutional 3D Network for Temporal Activity Detection. 5794–5803. 435 indexed citations breakdown →
14.
Panda, Rameswar, Abir Das, Ziyan Wu, Jan Ernst, & Amit K. Roy–Chowdhury. (2017). Weakly Supervised Summarization of Web Videos. 3677–3686. 66 indexed citations
15.
Panda, Rameswar, Abir Das, & Amit K. Roy–Chowdhury. (2016). Embedded sparse coding for summarizing multi-view videos. 191–195. 12 indexed citations
16.
Chakraborty, Anirban, Abir Das, & Amit K. Roy–Chowdhury. (2015). Network Consistent Data Association. IEEE Transactions on Pattern Analysis and Machine Intelligence. 38(9). 1859–1871. 28 indexed citations
17.
Das, Abir, Rameswar Panda, & Amit K. Roy–Chowdhury. (2015). Active image pair selection for continuous person re-identification. 4263–4267. 10 indexed citations
18.
Martinel, Niki, Abir Das, Christian Micheloni, & Amit K. Roy–Chowdhury. (2014). Re-Identification in the Function Space of Feature Warps. IEEE Transactions on Pattern Analysis and Machine Intelligence. 37(8). 1656–1669. 47 indexed citations
19.
Zhang, Shu, Abir Das, Chong Ding, & Amit K. Roy–Chowdhury. (2013). Online Social Behavior Modeling for Multi-target Tracking. 751–758. 6 indexed citations
20.
Das, Abir. (2003). Artificial bootstrap. iii. 255–261. 1 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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