Krishna, Ranjay

Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision

Multimodal Machine Learning ApplicationsImage Retrieval and Classification Techniques

Cited by 5,107

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 2,676

Image retrieval using scene graphs

Johnson, J., Krishna, R., Stark, M., Li, L.-J., Shamma, D. A., Bernstein, M. S., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Image Retrieval and Classification TechniquesAdvanced Image and Video Retrieval Techniques

Cited by 1,101

Explanations Can Reduce Overreliance on AI Systems During Decision-Making

Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M., & Krishna, R. (2022) · arXiv

Explainable Artificial Intelligence (XAI)Human-Automation Interaction and Safety

Cited by 403

Explanations Can Reduce Overreliance on AI Systems During Decision-Making

Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., & Krishna, R. (2023) · Proceedings of the ACM on Human-Computer Interaction

Explainable Artificial Intelligence (XAI)Human-Automation Interaction and Safety

Cited by 402

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Hsieh, C.-Y., Li, C.-L., Yeh, C.-K., Nakhost, H., Fujii, Y., Ratner, A., Krishna, R., Lee, C.-Y., & Pfister, T. (2023) · Findings of the Association for Computational Linguistics: ACL 2023

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 316

Conceptual Metaphors Impact Perceptions of Human-AI Collaboration

Khadpe, P., Krishna, R., Fei-Fei, L., Hancock, J. T., & Bernstein, M. S. (2020) · Proceedings of the ACM on Human-Computer Interaction

Social Robot Interaction and HRIAI in Service Interactions

Cited by 183

DataComp: In search of the next generation of multimodal datasets

Gadre, S. Y., Ilharco, G., Fang, A., Hayase, J., Smyrnis, G., Nguyen, T., Marten, R., Wortsman, M., Ghosh, D., Zhang, J., et al. (2023) · Advances in Neural Information Processing Systems 36

Multimodal Machine Learning ApplicationsImage Retrieval and Classification Techniques

Cited by 120

Holodeck: Language Guided Generation of 3D Embodied AI Environments

Yang, Y., Sun, F.-Y., Weihs, L., VanderBilt, E., Herrasti, A., Han, W., Wu, J., Haber, N., Krishna, R., Liu, L., et al. (2023) · arXiv

3D Shape Modeling and AnalysisArtificial Intelligence in Games

Cited by 101

Holodeck: Language Guided Generation of 3D Embodied AI Environments

Yang, Y., Sun, F.-Y., Weihs, L., Vanderbilt, E., Herrasti, A., Han, W., Wu, J., Haber, N., Krishna, R., Liu, L., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

3D Shape Modeling and AnalysisArtificial Intelligence in Games

Cited by 101

Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models

Deitke, M., Clark, C., Lee, S., Tripathi, R., Yang, Y., Park, J. S., Salehi, M., Muennighoff, N., Lo, K., Soldaini, L., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 67

Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models

Deitke, M., Clark, C., Lee, S., Tripathi, R., Yang, Y., Park, J. S., Salehi, M., Muennighoff, N., Lo, K., Soldaini, L., et al. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 67

Quilt-1M: One Million Image-Text Pairs for Histopathology

Ikezogwo, W. O., Seyfioglu, M. S., Ghezloo, F., Geva, D. S. C., Mohammed, F. S., Anand, P. K., Krishna, R., & Shapiro, L. (2023) · arXiv

Multimodal Machine Learning ApplicationsAI in cancer detection

Cited by 55

Found in the middle: Calibrating Positional Attention Bias Improves Long Context Utilization

Hsieh, C.-Y., Chuang, Y.-S., Li, C.-L., Wang, Z., Le, L., Kumar, A., Glass, J., Ratner, A., Lee, C.-Y., Krishna, R., et al. (2024) · Findings of the Association for Computational Linguistics ACL 2024

Topic ModelingNatural Language Processing Techniques

Cited by 31

THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation

Pumacay, W., Singh, I., Duan, J., Krishna, R., Thomason, J., & Fox, D. (2024) · Robotics: Science and Systems XX

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 29

THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation

Pumacay, W., Singh, I., Duan, J., Krishna, R., Thomason, J., & Fox, D. (2024) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 29

Offline Training of Language Model Agents with Functions as Learnable Weights

Zhang, S., Zhang, J., Liu, J., Song, L., Wang, C., Krishna, R., & Wu, Q. (2024) · arXiv

Topic ModelingArtificial Intelligence Applications

Cited by 17

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Hsieh, C.-Y., Li, C.-L., Yeh, C.-K., Nakhost, H., Fujii, Y., Ratner, A., Krishna, R., Lee, C.-Y., & Pfister, T. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 16

EcoAssistant: Using LLM Assistant More Affordably and Accurately

Zhang, J., Krishna, R., Awadallah, A. H., & Wang, C. (2023) · arXiv

Topic ModelingSoftware Engineering Research

Cited by 8

RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics

Yuan, W., Duan, J., Blukis, V., Pumacay, W., Krishna, R., Murali, A., Mousavian, A., & Fox, D. (2024) · arXiv

Robot Manipulation and LearningMultimodal Machine Learning Applications

Cited by 7

AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation

Duan, J., Pumacay, W., Kumar, N., Wang, Y. R., Tian, S., Yuan, W., Krishna, R., Fox, D., Mandlekar, A., & Guo, Y. (2024) · arXiv

Robot Manipulation and LearningMultimodal Machine Learning Applications

Cited by 5

Spurious Rewards: Rethinking Training Signals in RLVR

Shao, R., Li, S. S., Xin, R., Geng, S., Wang, Y., Oh, S., Du, S. S., Lambert, N., Min, S., Krishna, R., et al. (2025) · arXiv

Reinforcement Learning in RoboticsTopic Modeling

Cited by 1

Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization

Hsieh, C.-Y., Chuang, Y.-S., Li, C.-L., Wang, Z., Le, L. T., Kumar, A., Glass, J., Ratner, A., Lee, C.-Y., Krishna, R., et al. (2024) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 0

JobBench: Aligning Agent Work With Human Will

Li, Y., Feng, Y., Xu, Z., Ma, Z., Zheng, K., Jiang, F., Sun, X., Shao, R., Chen, Z., Huang, Y., et al. (2026) · arXiv

Ethics and Social Impacts of AIAI and HR Technologies

Cited by 0