Princeton Visual AI Lab



  1. Vision-Language Dataset Distillation

    Xindi Wu, Byron Zhang, Zhiwei Deng and Olga Russakovsky.



    [paper] [code] [bibtex]
  2. Overcoming Bias in Pretrained Models by Manipulating the Finetuning Dataset

    Angelina Wang and Olga Russakovsky.

    International Conference on Computer Vision (ICCV), 2023.

    [paper] [code] [bibtex]
  3. Gender Artifacts in Visual Datasets

    Nicole Meister*, Dora Zhao*, Angelina Wang, Vikram V. Ramaswamy, Ruth Fong and Olga Russakovsky.

    International Conference on Computer Vision (ICCV), 2023.

    [paper] [code] [bibtex]
  4. Art and the Science of Generative AI

    Ziv Epstein, Aaron Hertzmann and the Investigators of Human Creativity (Memo Akten, Hany Farid, Jessica Fjeld, Morgan R. Frank, Matthew Groh, Laura Herman, Neil Leach, Robert Mahari, Alex Pentland, Olga Russakovsky, Hope Schroeder, Amy Smith).

    Science Perspectives, 2023.

    [paper] [extended white paper] [bibtex]
  5. Discrete Diffusion Reward Guidance Methods for Offline Reinforcement Learning

    Matthew Coleman, Olga Russakovsky, Christine Allen-Blanchette and Ye Zhu.



    [paper]
  6. ICON2: Reliably Benchmarking Predictive Inequity in Object Detection

    Sruthi Sudhakar, Viraj Prabhu, Olga Russakovsky and Judy Hoffman.



    [paper] [bibtex]
  7. Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application

    Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong and Andrés Monroy-Hernández.

    ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2023.

    [paper] [bibtex]
  8. UFO: A Unified Method for Controlling Understandability and Faithfulness Objectives in Concept-based Explanations for CNNs

    Vikram V. Ramaswamy, Sunnie S. Y. Kim, Ruth Fong and Olga Russakovsky.



    [paper] [bibtex]
  9. Overlooked Factors in Concept-based Explanations: Dataset Choice, Concept Learnability, and Human Capability

    Vikram V. Ramaswamy, Sunnie S. Y. Kim, Ruth Fong and Olga Russakovsky.

    Computer Vision and Pattern Recognition (CVPR), 2023.

    [paper] [code] [bibtex]
  10. "Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction

    Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong and Andrés Monroy-Hernández.

    ACM Conference on Human Factors in Computing Systems (CHI), 2023.

    [paper] [supplement] [30-sec video] [10-min video] [bibtex]
  11. ELUDE: Generating Interpretable Explanations via a Decomposition into Labelled and Unlabelled Features

    Vikram V. Ramaswamy, Sunnie S. Y. Kim, Nicole Meister, Ruth Fong and Olga Russakovsky.



    [paper] [bibtex]
  12. Learning Actionness from Action/Background Discrimination

    Ozge Yalcinkaya Simsek, Olga Russakovsky and Pinar Duygulu.

    Signal, Image and Video Processing (SIViP), 2022.

    [paper] [bibtex]
  13. Enabling Detailed Action Recognition Evaluation Through Video Dataset Augmentation

    Jihoon Chung, Yu Wu and Olga Russakovsky.

    Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, 2022.

    [paper] [code] [bibtex]
  14. Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks

    Zhiwei Deng and Olga Russakovsky.

    Neural Information Processing Systems (NeurIPS), 2022.

    [paper] [code] [bibtex]
  15. HIVE: Evaluating the Human Interpretability of Visual Explanations

    Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Ruth Fong and Olga Russakovsky.

    European Conference on Computer Vision (ECCV), 2022.

    [paper] [website] [code] [extended abstract] [2-min video] [8-min video] [bibtex]
  16. Multi-Query Video Retrieval

    Zeyu Wang, Yu Wu, Karthik Narasimhan and Olga Russakovsky.

    European Conference on Computer Vision (ECCV), 2022.

    [paper] [code] [bibtex]
  17. Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation

    Angelina Wang, Vikram V. Ramaswamy, and Olga Russakovsky.

    ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2022.

    [paper] [code] [bibtex]
  18. CARETS: A Consistency And Robustness Evaluative Test Suite for VQA

    Carlos E. Jimenez, Olga Russakovsky and Karthik Narasimhan.

    Association for Computational Linguistics (ACL), 2022.

    [paper] [code] [bibtex]
  19. A Study of Face Obfuscation in ImageNet

    Kaiyu Yang, Jacqueline Yau, Li Fei-Fei, Jia Deng and Olga Russakovsky.

    International Conference on Machine Learning (ICML), 2022.

    [paper] [project] [code] [bibtex]
  20. Understanding and Evaluating Racial Biases in Image Captioning

    Dora Zhao, Angelina Wang and Olga Russakovsky.

    International Conference on Computer Vision (ICCV), 2021.

    [paper] [website] [code] [bibtex]
  21. Point and Ask: Incorporating Pointing into Visual Question Answering

    Arjun Mani, Nobline Yoo, Will Hinthorn and Olga Russakovsky.


    [paper] [website] [code] [bibtex]
  22. Directional Bias Amplification

    Angelina Wang and Olga Russakovsky.

    International Conference on Machine Learning (ICML), 2021.

    [paper] [code] [bibtex]
  23. [Re] Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias

    Sunnie S. Y. Kim, Sharon Zhang, Nicole Meister and Olga Russakovsky.

    ReScience C, 2021. ML Reproducibility Challenge, 2020.

    [paper] [code] [bibtex]
  24. Fair Attribute Classification through Latent Space De-biasing

    Vikram V. Ramaswamy, Sunnie S. Y. Kim and Olga Russakovsky.

    Computer Vision and Pattern Recognition (CVPR), 2021.

    [paper] [website] [code] [bibtex]
  25. Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation

    Zhiwei Deng, Karthik Narasimhan and Olga Russakovsky.

    Neural Information Processing Systems (NeurIPS), 2020.

    [paper] [bibtex]
  26. CornerNet-Lite: Efficient Keypoint Based Object Detection

    Hei Law, Yun Teng, Olga Russakovsky and Jia Deng.

    British Machine Vision Conference (BMVC), 2020.

    [paper] [code] [bibtex]
  27. REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets

    Angelina Wang, Arvind Narayanan and Olga Russakovsky.

    European Conference on Computer Vision (ECCV), 2020.

    [paper] [code] [video] [90 sec video] [10-min video] [bibtex]
  28. Towards Unique and Informative Captioning of Images

    Zeyu Wang, Berthy Feng, Karthik Narasimhan and Olga Russakovsky.

    European Conference on Computer Vision (ECCV), 2020.

    [paper] [code] [1-min video] [10-min video] [bibtex]
  29. Take The Scenic Route: Improving Generalization In Vision-and-language Navigation

    Felix Yu, Zhiwei Deng, Karthik Narasimhan and Olga Russakovsky.

    CVPR Visual Learning with Limited Labels Workshop (CVPRW), 2020.

    [paper] [video] [code] [bibtex]
  30. Towards Fairness In Visual Recognition: Effective Strategies For Bias Mitigation

    Zeyu Wang, Klint Qinami, Ioannis C. Karakozis, Kyle Genova, Prem Nair, Kenji Hata and Olga Russakovsky.

    Computer Vision and Pattern Recognition (CVPR), 2020.

    [paper] [code] [1-min video] [bibtex]
  31. Towards Fairer Datasets: Filtering And Balancing The Distribution Of The People Subtree In The Imagenet Hierarchy

    Kaiyu Yang, Klint Qinami, Li Fei-Fei, Jia Deng and Olga Russakovsky.

    Conference on Fairness, Accountability and Transparency (FAT*), 2020.

    [paper] [project] [Wired article] [bibtex]
  32. Compositional Temporal Visual Grounding of Natural Language Event Descriptions

    Jonathan Stroud, Ryan McCaffrey, Rada Mihalcea, Jia Deng and Olga Russakovsky.

    Preprint on arxiv, 2019.

    [paper] [project] [bibtex]
  33. Human Uncertainty Makes Classification More Robust

    Joshua C. Peterson*, Ruairidh M. Battleday*, Thomas L. Griffiths and Olga Russakovsky. (* = equal contribution)

    International Conference on Computer Vision (ICCV), 2019.

    [paper] [bibtex]
  34. An Adversarially Crowdsourced Benchmark For Spatial Relation Recognition

    Kaiyu Yang, Olga Russakovsky and Jia Deng.

    International Conference on Computer Vision (ICCV), 2019.

    [paper] [bibtex]
  35. The more you look, the more you see: towards general object understanding through recursive refinement

    Jingyan Wang, Olga Russakovsky and Deva Ramanan.

    Winter Conference on Applications of Computer Vision (WACV), 2018.

    [paper] [supplement] [code] [bibtex]
  36. What Actions Are Needed For Understanding Human Actions In Videos?

    Gunnar Sigurdsson, Olga Russakovsky and Abhinav Gupta.

    International Conference on Computer Vision (ICCV), 2017.

    [paper] [bibtex]
  37. Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos

    Serena Yeung, Olga Russakovsky, Ning Jin, Mykhaylo Andriluka, Greg Mori and Li Fei-Fei.

    International Journal of Computer Vision (IJCV), 2017.

    [paper] [project] [bibtex]
  38. What's in a Question: Using Visual Questions as a Form of Supervision

    Siddha Ganju, Olga Russakovsky and Abhinav Gupta.

    Computer Vision and Pattern Recognition (CVPR), 2017.

    [paper] [project] [bibtex]
  39. Predictive-Corrective Networks for Action Detection

    Achal Dave, Olga Russakovsky and Deva Ramanan.

    Computer Vision and Pattern Recognition (CVPR), 2017.

    [paper] [project] [bibtex]
  40. Learning to Learn from Noisy Web Videos

    Serena Yeung, Vignesh Ramanathan, Olga Russakovsky, Liyue Shen, Greg Mori and Li Fei-Fei.

    Computer Vision and Pattern Recognition (CVPR), 2017.

    [paper] [poster] [bibtex]
  41. Crowdsourcing in Computer Vision
    Adriana Kovashka, Olga Russakovsky, Li Fei-Fei and Kristen Grauman.

    Foundation and Trends in Computer Vision and Graphics, 2016.

    [paper] [bibtex]
  42. Much Ado About Time: Exhaustive Annotation of Temporal Data

    Gunnar A. Sigurdsson, Olga Russakovsky, Ali Farhadi, Ivan Laptev and Abhinav Gupta.

    Conference on Human Computation and Crowdsourcing (HCOMP), 2016.

    [paper] [project] [poster] [slides key, slides pdf] [bibtex]
  43. What's the Point: Semantic Segmentation with Point Supervision

    Amy Bearman, Olga Russakovsky, Vittorio Ferrari and Li Fei-Fei.

    European Conference on Computer Vision (ECCV), 2016.

    [paper] [project] [bibtex]
  44. End-to-end Learning of Action Detection from Frame Glimpses in Videos

    Serena Yeung, Olga Russakovsky, Greg Mori and Li Fei-Fei.

    Computer Vision and Pattern Recognition (CVPR), 2016.

    [paper] [project] [bibtex]
  45. Towards More Gender Diversity in CS through an Artificial Intelligence Summer Program for High School Girls

    Marie E. Vachovsky, Grace Wu, Sorathan Chaturapruek, Olga Russakovsky, Rick Sommer, Li Fei-Fei.

    Special Interest Group on Computer Science Education (SIGCSE), 2016.

    [paper] [SAILORS camp homepage] [Wired article] [bibtex]
  46. Scaling up Object Detection

    Olga Russakovsky.

    PhD Thesis, Stanford University, 2015.

    [paper] [poster] [bibtex]
  47. Best of both worlds: human-machine collaboration for object annotation

    Olga Russakovsky, Li-Jia Li and Li Fei-Fei.

    Computer Vision and Pattern Recognition (CVPR), 2015.

    [paper] [project] [bibtex]
  48. Joint calibration of Ensemble of Exemplar SVMs

    Davide Modolo, Alexander Vezhnevets, Olga Russakovsky and Vittorio Ferrari.

    Computer Vision and Pattern Recognition (CVPR), 2015.

    [paper] [code] [bibtex]
  49. ImageNet Large Scale Visual Recognition Challenge

    Olga Russakovsky*, Jia Deng*, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander Berg and Li Fei-Fei.

    (* = equal contribution)
    International Journal of Computer Vision (IJCV), 2015.

    [paper] [paper content on arxiv] [bibtex] [browse detection data] [attribute annotations] [ILSVRC homepage]
  50. Scalable Multi-Label Annotation

    Jia Deng, Olga Russakovsky, Jonathan Krause, Michael Bernstein, Alexander Berg and Li Fei-Fei.

    ACM Conference on Human Factors in Computing Systems (CHI), 2014.

    [paper] [bibtex] [slides]
  51. Detecting avocados to zucchinis: what have we done, and where are we going?

    Olga Russakovsky, Jia Deng, Zhiheng Huang, Alexander Berg, Li Fei-Fei.

    International Conference on Computer Vision (ICCV), 2013.

    [pdf] [bibtex] [supplement] [additional analysis] [attribute annotations] [poster] [video of talk at BAVM] [slides pptx, slides pdf]
  52. Object-centric spatial pooling for image classification

    Olga Russakovsky, Yuanqing Lin, Kai Yu and Li Fei-Fei.

    European Conference on Computer Vision (ECCV), 2012.

    [pdf] [bibtex] [poster] [slides pptx, slides pdf] [30-sec spotlight] [FAQ]
  53. Attribute learning in large-scale data

    Olga Russakovsky and Li Fei-Fei.

    Parts and Attributes Workshop at European Conference on Computer Vision (ECCVW), 2010.

    [pdf] [bibtex] [slides odp, slides pdf] [data]
  54. A Steiner tree approach to efficient object detection.

    Olga Russakovsky and Andrew Y. Ng.

    Computer Vision and Pattern Recognition (CVPR), 2010.

    [paper] [bibtex] [poster] [data]
  55. Autonomous operation of novel elevators for robot navigation

    Ellen Klingbeil, Blake Carpenter, Olga Russakovsky, Andrew Y. Ng.

    International Conference on Robotics and Automation (ICRA), 2010.

    [paper] [bibtex]
  56. STanford AI Robot (STAIR) Vision Library

    Stephen Gould, Olga Russakovsky, Ian Goodfellow, Paul Baumstarck, Andrew Y. Ng and Daphne Koller.

    http://ai.stanford.edu/~sgould/svl, 2010.

    [project] [bibtex] [code]
  57. Training Conditional Random Fields for maximum labelwise accuracy

    Samuel S. Gross, Olga Russakovsky, Chuong B. Do and Serafim Batzoglou.

    Advances in Neural Information Processing Systems (NeurIPS), 2007.

    [paper] [bibtex]

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