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.
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
201712.3k citationsRamprasaath R. Selvaraju, Michael Cogswell et al.profile →
VQA: Visual Question Answering
20152.3k citationsJiasen Lu, Dhruv Batra et al.profile →
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
20171.1k citationsDhruv Batra, Devi Parikh et al.profile →
Joint Unsupervised Learning of Deep Representations and Image Clusters
2016444 citationsJianwei Yang, Devi Parikh et al.profile →
This map shows the geographic impact of Dhruv Batra'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 Dhruv Batra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dhruv Batra more than expected).
This network shows the impact of papers produced by Dhruv Batra. 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 Dhruv Batra. The network helps show where Dhruv Batra may publish in the future.
Co-authorship network of co-authors of Dhruv Batra
This figure shows the co-authorship network connecting the top 25 collaborators of Dhruv Batra.
A scholar is included among the top collaborators of Dhruv Batra 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 Dhruv Batra. Dhruv Batra is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kalyan, Ashwin, Stefan Lee, Anitha Kannan, & Dhruv Batra. (2018). Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations. International Conference on Machine Learning. 2449–2458.1 indexed citations
11.
Ke, Nan Rosemary, Amanpreet Singh, Abdelaziz Touati, et al.. (2018). Modeling the Long Term Future in Model-Based Reinforcement Learning. International Conference on Learning Representations.5 indexed citations
12.
Kalyan, Ashwin, et al.. (2018). Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples. International Conference on Learning Representations.8 indexed citations
13.
Lu, Jiasen, Anitha Kannan, Jianwei Yang, Devi Parikh, & Dhruv Batra. (2017). Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model. Neural Information Processing Systems. 30. 314–324.37 indexed citations
14.
Selvaraju, Ramprasaath R., Abhishek Das, Ramakrishna Vedantam, et al.. (2016). Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization. arXiv (Cornell University). 1.44 indexed citations
15.
Sun, Qing & Dhruv Batra. (2015). SubmodBoxes: near-optimal search for a set of diverse object proposals. Neural Information Processing Systems. 28. 1378–1386.5 indexed citations
16.
Kohli, Pushmeet, et al.. (2014). Efficiently Enforcing Diversity in Multi-Output Structured Prediction. International Conference on Artificial Intelligence and Statistics. 33. 284–292.17 indexed citations
17.
Batra, Dhruv, et al.. (2013). Group Norm for Learning Structured SVMs with Unstructured Latent Variables. DSpace@MIT (Massachusetts Institute of Technology).1 indexed citations
18.
Kohli, Pushmeet, et al.. (2013). DivMCuts: Faster Training of Structural SVMs with Diverse M-Best Cutting-Planes. International Conference on Artificial Intelligence and Statistics. 31. 316–324.8 indexed citations
19.
Batra, Dhruv, et al.. (2012). Multiple Choice Learning: Learning to Produce Multiple Structured Outputs. Neural Information Processing Systems. 25. 1799–1807.82 indexed citations
20.
Tarlow, Daniel, Dhruv Batra, Pushmeet Kohli, & Vladimir Kolmogorov. (2011). Dynamic Tree Block Coordinate Ascent. International Conference on Machine Learning. 113–120.14 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.