Ramakrishna Vedantam Google Scholar: h-index, Total Citations, and Citation Map
Ramakrishna Vedantam's h-index is 20 (20 i10-index, 56,302+ total citations across 23+ publications) according to Google Scholar as of October 2026.
Ramakrishna Vedantam is a researcher affiliated with their institution, specializing in Deep Learning, Computer Vision, Machine Learning. Their work has been cited 56,302 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Ramakrishna Vedantam's Citation Metrics
Bibliometric impact based on 23 indexed publications.
- H-Index
- 20
- i10-Index
- 20
- Total Citations
- 56,302
- Citing Countries
- 59
As of October 2026.
Ramakrishna Vedantam has an h-index of 20 and 56,302 total citations across 23 publications, with research cited by institutions in 59 countries.
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Grad-CAM: Why did you say that?
201643,559
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
28 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered Grad-CAM for visualizing deep learning decisions, establishing a foundational method for model interpretability that has been widely adopted and extended by the independent research community.
The researcher advanced object counting in complex scenes and extended this framework to benchmark concept learning under uncertainty, establishing a foundational line of work in robust visual perception.
The researcher pioneered visually grounded word embeddings via Visual Word2Vec, establishing a framework for multimodal semantic representation that was subsequently extended to auditory domains.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Ramakrishna Vedantam's research
Ramakrishna Vedantam is a researcher working on Deep Learning, Computer Vision and Machine Learning. Their work has been cited 56,302 times across 23 publications (h-index 20), according to Google Scholar.
Their most-cited work, “Grad-CAM: Why did you say that?” (2016), has accumulated 43,559 citations. Other influential works include “Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization” (2017) with 20,464 citations and “CIDEr: Consensus-based Image Description Evaluation” (2014) with 7,364 citations.
Citations of Ramakrishna Vedantam's research come primarily from United States, China and United Kingdom, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
