Abhinav Shrivastava

7.7k citations
73 papers · 3.2k indexed · 2 hit papers · h-index 20

Abhinav Shrivastava

67 papers receiving 3.1k citations

Hit Papers

Revisiting Unreasonable Effectiveness of Data in Deep Lea...1.3k20172026202020234008001.2k

Peers

Abhinav Shrivastava
Comparison fields: 5 of 159
  • Computer Vision and Pattern Recognition 2.1k
  • Artificial Intelligence 1.3k
  • Media Technology 272
  • Health Informatics 35
  • Signal Processing 145
Replace Mathilde Caron with:
Mathilde Caron United States
Yanming Guo China
Hugo Touvron France
Chen Sun China
Songyang Lao China
Xinlei Chen China
Hongtao Xie China
Radu Tudor Ionescu Romania
Ishan Misra United States
Jordi Pont-Tuset Spain
Abhinav Shrivastava relative to Mathilde Caron United States Mathilde Caron's profile →
Citations per field
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Mathilde Caron · 1×
Citations per year

Countries citing papers authored by Abhinav Shrivastava

Since Specialization
Citations

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

Fields of papers citing papers by Abhinav Shrivastava

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Abhinav Shrivastava, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Abhinav Shrivastava Line = papers co-authored together Abhinav Shrivastava links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20241
2 20242
3 20246
4 20241
5 20240
6 20243
7 20240
8 20244
9 20236
10 20237
11 202120
12 20214
13 20211
14 20198
15
Revisiting Unreasonable Effectiveness of Data in Deep Learning Erabreakdown →
20171326
16
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detectionbreakdown →
2017425
17 20154
18 2013245
19
Exemplar-SVMs for visual object detection, label transfer and image retrieval
20126
20 201118

About Abhinav Shrivastava

Abhinav Shrivastava is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing, having authored 73 papers that have together received 3.2k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (20 papers), Multimodal Machine Learning Applications (19 papers), Advanced Image and Video Retrieval Techniques (17 papers), Advanced Neural Network Applications (14 papers), Generative Adversarial Networks and Image Synthesis (13 papers), Human Pose and Action Recognition (11 papers), Advanced Vision and Imaging (9 papers) and Anomaly Detection Techniques and Applications (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.1k citations), Artificial Intelligence (1.3k citations) and Media Technology (272 citations). Abhinav Shrivastava has collaborated with scholars based in United States, India and China. Frequent co-authors include Abhinav Gupta, Saurabh Singh, Chen Sun, Xiaolong Wang, Xinlei Chen, Ser-Nam Lim, Larry S. Davis, Alexei A. Efros, Tomasz Malisiewicz and Zuxuan Wu. Their work appears in journals such as Nature Machine Intelligence, IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Imaging, Frontiers in Computational Neuroscience and International Journal of Engineering and Advanced Technology.

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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