Abhinav Gupta

74 papers receiving 7.6k citations

Hit Papers

Unsupervised Visual Representation Learning by Context Pr...2011202620162021201520172011201520174008001.2k

Peers

Abhinav Gupta
Comparison fields: 5 of 182
  • Computer Vision and Pattern Recognition 5.3k
  • Artificial Intelligence 3.7k
  • Aerospace Engineering 562
  • Media Technology 502
  • Radiology, Nuclear Medicine and Imaging 486
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Citations per year

Countries citing papers authored by Abhinav Gupta

Since Specialization
Citations

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

Fields of papers citing papers by Abhinav Gupta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Abhinav Gupta

This figure shows the co-authorship network connecting the top 25 collaborators of Abhinav Gupta. A scholar is included among the top collaborators of Abhinav Gupta 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 Abhinav Gupta. Abhinav Gupta 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
#WorkIndexed citations
1 2
2 7
3 41
4 53
5
Object Goal Navigation using Goal-Oriented Semantic Exploration
20
6
Dynamics-Aware Embeddings
1
7
Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases
6
8
Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies
2
9
Bounce and Learn: Modeling Scene Dynamics with Real-World Bounces
2
10
Beyond Grids: Learning Graph Representations for Visual Recognition
98
11 158
12
Revisiting Unreasonable Effectiveness of Data in Deep Learning Erabreakdown →
1326
13
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detectionbreakdown →
425
14 76
15
Unsupervised Visual Representation Learning by Context Predictionbreakdown →
1365
16 95
17 4
18
Mid-level Visual Element Discovery as Discriminative Mode Seeking
149
19 245
20
Exemplar-SVMs for visual object detection, label transfer and image retrieval
6

About Abhinav Gupta

Abhinav Gupta is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Graphics and Computer-Aided Design, having authored 76 papers that have together received 7.9k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (17 papers), Advanced Image and Video Retrieval Techniques (17 papers) and Human Pose and Action Recognition (15 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (5.3k citations), Artificial Intelligence (3.7k citations) and Media Technology (502 citations). Abhinav Gupta has collaborated with scholars based in United States, Israel and India. Frequent co-authors include Alexei A. Efros, Abhinav Shrivastava, Carl Doersch, Xiaolong Wang, Saurabh Singh, Chen Sun, Tomasz Malisiewicz, Martial Hebert, Yufei Ye and Xinlei Chen. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision and Journal of International Economics.

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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