Aneeshan Sain

1.2k total citations
38 papers, 688 citations indexed

About

Aneeshan Sain is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Aneeshan Sain has authored 38 papers receiving a total of 688 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Computer Vision and Pattern Recognition, 11 papers in Artificial Intelligence and 4 papers in Computational Mechanics. Recurrent topics in Aneeshan Sain's work include Advanced Image and Video Retrieval Techniques (20 papers), Multimodal Machine Learning Applications (15 papers) and Domain Adaptation and Few-Shot Learning (9 papers). Aneeshan Sain is often cited by papers focused on Advanced Image and Video Retrieval Techniques (20 papers), Multimodal Machine Learning Applications (15 papers) and Domain Adaptation and Few-Shot Learning (9 papers). Aneeshan Sain collaborates with scholars based in United Kingdom, China and India. Aneeshan Sain's co-authors include Yi-Zhe Song, Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Tao Xiang, Subhadeep Koley, Yongxin Yang, Ayan Kumar Bhunia, Zhanyu Ma, Amandeep Kumar and Partha Pratim Roy and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Pattern Recognition.

In The Last Decade

Aneeshan Sain

35 papers receiving 672 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Aneeshan Sain United Kingdom 16 566 181 58 50 43 38 688
Ayan Kumar Bhunia United Kingdom 17 787 1.4× 293 1.6× 53 0.9× 63 1.3× 52 1.2× 30 970
Pinaki Nath Chowdhury United Kingdom 15 474 0.8× 127 0.7× 35 0.6× 57 1.1× 34 0.8× 36 605
Duanqing Xu China 10 582 1.0× 180 1.0× 30 0.5× 45 0.9× 22 0.5× 41 720
Nanxuan Zhao Hong Kong 15 371 0.7× 99 0.5× 20 0.3× 46 0.9× 30 0.7× 36 507
Ben Daubney United Kingdom 7 434 0.8× 76 0.4× 64 1.1× 34 0.7× 22 0.5× 12 535
Sara Sabour United States 5 269 0.5× 220 1.2× 27 0.5× 24 0.5× 18 0.4× 5 462
Jifei Song United Kingdom 9 528 0.9× 104 0.6× 16 0.3× 59 1.2× 13 0.3× 12 574
Yuheng Li United States 6 502 0.9× 266 1.5× 24 0.4× 50 1.0× 12 0.3× 11 772
Chengying Gao China 11 310 0.5× 125 0.7× 18 0.3× 80 1.6× 16 0.4× 40 465

Countries citing papers authored by Aneeshan Sain

Since Specialization
Citations

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

Fields of papers citing papers by Aneeshan Sain

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aneeshan Sain

This figure shows the co-authorship network connecting the top 25 collaborators of Aneeshan Sain. A scholar is included among the top collaborators of Aneeshan Sain 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 Aneeshan Sain. Aneeshan Sain 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.
Liu, Zhongyuan, Dongliang Chang, Aneeshan Sain, et al.. (2025). Self-randomized focuses effectively boost metric-based few-shot classifiers. Pattern Recognition. 164. 111538–111538.
2.
Koley, Subhadeep, Tapas Kumar Dutta, Aneeshan Sain, et al.. (2025). SketchFusion: Learning Universal Sketch Features through Fusing Foundation Models. 2556–2567.
3.
Du, Ruoyi, Aneeshan Sain, Kongming Liang, et al.. (2024). Understanding Episode Hardness in Few-Shot Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(1). 616–633.
4.
Koley, Subhadeep, Ayan Kumar Bhunia, Aneeshan Sain, et al.. (2024). How to Handle Sketch-Abstraction in Sketch-Based Image Retrieval?. 16859–16869. 12 indexed citations
5.
Koley, Subhadeep, Ayan Kumar Bhunia, Aneeshan Sain, et al.. (2024). You'll Never Walk Alone: A Sketch and Text Duet for Fine-Grained Image Retrieval. 16509–16519. 11 indexed citations
6.
Koley, Subhadeep, Ayan Kumar Bhunia, Aneeshan Sain, et al.. (2024). Text-to-Image Diffusion Models are Great Sketch-Photo Matchmakers. 16826–16837. 7 indexed citations
7.
Koley, Subhadeep, Ayan Kumar Bhunia, Aneeshan Sain, et al.. (2024). It's All About Your Sketch: Democratising Sketch Control in Diffusion Models. 7204–7214. 9 indexed citations
8.
Chowdhury, Pinaki Nath, Ayan Kumar Bhunia, Aneeshan Sain, et al.. (2023). SceneTrilogy: On Human Scene-Sketch and its Complementarity with Photo and Text. 10972–10983. 14 indexed citations
9.
Koley, Subhadeep, Ayan Kumar Bhunia, Aneeshan Sain, et al.. (2023). Picture that Sketch: Photorealistic Image Generation from Abstract Sketches. 6850–6861. 37 indexed citations
10.
Sain, Aneeshan, Ayan Kumar Bhunia, Pinaki Nath Chowdhury, et al.. (2023). CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not. 2765–2775. 69 indexed citations
11.
Chowdhury, Pinaki Nath, Ayan Kumar Bhunia, Aneeshan Sain, et al.. (2023). What Can Human Sketches Do for Object Detection?. 15083–15094. 28 indexed citations
12.
Chang, Dongliang, Aneeshan Sain, Xiaoxu Li, et al.. (2023). Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification. Proceedings of the AAAI Conference on Artificial Intelligence. 37(3). 2821–2829. 37 indexed citations
13.
Bhunia, Ayan Kumar, Subhadeep Koley, Aneeshan Sain, et al.. (2022). Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 989–998. 41 indexed citations
14.
Sain, Aneeshan, et al.. (2022). Sketch3T: Test-Time Training for Zero-Shot SBIR. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 7452–7461. 35 indexed citations
15.
Bhunia, Ayan Kumar, Aneeshan Sain, Pinaki Nath Chowdhury, & Yi-Zhe Song. (2021). Text is Text, No Matter What: Unifying Text Recognition using Knowledge Distillation. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 963–972. 19 indexed citations
16.
Sain, Aneeshan, Ayan Kumar Bhunia, Yongxin Yang, Tao Xiang, & Yi-Zhe Song. (2021). StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval. 8500–8509. 73 indexed citations
17.
Bhunia, Ayan Kumar, Pinaki Nath Chowdhury, Aneeshan Sain, et al.. (2021). More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval. 4245–4254. 45 indexed citations
18.
Qi, Yonggang, et al.. (2021). PQA: Perceptual Question Answering. 12051–12059. 2 indexed citations
19.
Sain, Aneeshan, et al.. (2020). S3Net:Graph Representational Network For Sketch Recognition. 1–6. 7 indexed citations
20.
Sain, Aneeshan, et al.. (2018). Background Subtraction Based on Integration of Alternative Cues in Freely Moving Camera. IEEE Transactions on Circuits and Systems for Video Technology. 29(7). 1933–1945. 12 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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