no code implementations • 13 Dec 2022 • Zixian Ma, Jerry Hong, Mustafa Omer Gul, Mona Gandhi, Irena Gao, Ranjay Krishna
To measure systematicity, CREPE consists of three test datasets.
no code implementations • 13 Dec 2022 • Helena Vasconcelos, Matthew Jörke, Madeleine Grunde-McLaughlin, Tobias Gerstenberg, Michael Bernstein, Ranjay Krishna
Prior work has identified a resilient phenomenon that threatens the performance of human-AI decision-making teams: overreliance, when people agree with an AI, even when it is incorrect.
1 code implementation • 9 Oct 2022 • Zixian Ma, Rose Wang, Li Fei-Fei, Michael Bernstein, Ranjay Krishna
These results identify tasks where expectation alignment is a more useful strategy than curiosity-driven exploration for multi-agent coordination, enabling agents to do zero-shot coordination.
no code implementations • CVPR 2022 • Mona Gandhi, Mustafa Omer Gul, Eva Prakash, Madeleine Grunde-McLaughlin, Ranjay Krishna, Maneesh Agrawala
Recent video question answering benchmarks indicate that state-of-the-art models struggle to answer compositional questions.
no code implementations • 12 Apr 2022 • Madeleine Grunde-McLaughlin, Ranjay Krishna, Maneesh Agrawala
Prior benchmarks have analyzed models' answers to questions about videos in order to measure visual compositional reasoning.
no code implementations • 12 Nov 2021 • Ranjay Krishna, Mitchell Gordon, Li Fei-Fei, Michael Bernstein
Over the last decade, Computer Vision, the branch of Artificial Intelligence aimed at understanding the visual world, has evolved from simply recognizing objects in images to describing pictures, answering questions about images, aiding robots maneuver around physical spaces and even generating novel visual content.
no code implementations • 16 Aug 2021 • Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, aditi raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
1 code implementation • ACL 2021 • Siddharth Karamcheti, Ranjay Krishna, Li Fei-Fei, Christopher D. Manning
Active learning promises to alleviate the massive data needs of supervised machine learning: it has successfully improved sample efficiency by an order of magnitude on traditional tasks like topic classification and object recognition.
no code implementations • CVPR 2021 • Madeleine Grunde-McLaughlin, Ranjay Krishna, Maneesh Agrawala
AGQA contains $192M$ unbalanced question answer pairs for $9. 6K$ videos.
1 code implementation • EMNLP (WNUT) 2020 • Rachel Gardner, Maya Varma, Clare Zhu, Ranjay Krishna
Datasets extracted from social networks and online forums are often prone to the pitfalls of natural language, namely the presence of unstructured and noisy data.
no code implementations • 5 Aug 2020 • Pranav Khadpe, Ranjay Krishna, Li Fei-Fei, Jeffrey Hancock, Michael Bernstein
In a third study, we assess effects of metaphor choices on potential users' desire to try out the system and find that users are drawn to systems that project higher competence and warmth.
1 code implementation • 15 Dec 2019 • Jingwei Ji, Ranjay Krishna, Li Fei-Fei, Juan Carlos Niebles
Next, by decomposing and learning the temporal changes in visual relationships that result in an action, we demonstrate the utility of a hierarchical event decomposition by enabling few-shot action recognition, achieving 42. 7% mAP using as few as 10 examples.
no code implementations • 2 Dec 2019 • Khaled Jedoui, Ranjay Krishna, Michael Bernstein, Li Fei-Fei
The assumption that these tasks always have exactly one correct answer has resulted in the creation of numerous uncertainty-based measurements, such as entropy and least confidence, which operate over a model's outputs.
no code implementations • 12 Jun 2019 • Apoorva Dornadula, Austin Narcomey, Ranjay Krishna, Michael Bernstein, Li Fei-Fei
We introduce the first scene graph prediction model that supports few-shot learning of predicates.
1 code implementation • ICCV 2019 • Vincent S. Chen, Paroma Varma, Ranjay Krishna, Michael Bernstein, Christopher Re, Li Fei-Fei
All scene graph models to date are limited to training on a small set of visual relationships that have thousands of training labels each.
Ranked #1 on
Scene Graph Detection
on VRD
no code implementations • NeurIPS 2019 • Sharon Zhou, Mitchell L. Gordon, Ranjay Krishna, Austin Narcomey, Li Fei-Fei, Michael S. Bernstein
We construct Human eYe Perceptual Evaluation (HYPE) a human benchmark that is (1) grounded in psychophysics research in perception, (2) reliable across different sets of randomly sampled outputs from a model, (3) able to produce separable model performances, and (4) efficient in cost and time.
no code implementations • CVPR 2019 • Ranjay Krishna, Michael Bernstein, Li Fei-Fei
We build a model that maximizes mutual information between the image, the expected answer and the generated question.
no code implementations • ICLR Workshop DeepGenStruct 2019 • Sharon Zhou, Mitchell Gordon, Ranjay Krishna, Austin Narcomey, Durim Morina, Michael S. Bernstein
The second, HYPE-Infinity, measures human error rate on fake and real images with no time constraints, maintaining stability and drastically reducing time and cost.
no code implementations • 11 Aug 2018 • Bernard Ghanem, Juan Carlos Niebles, Cees Snoek, Fabian Caba Heilbron, Humam Alwassel, Victor Escorcia, Ranjay Krishna, Shyamal Buch, Cuong Duc Dao
The guest tasks focused on complementary aspects of the activity recognition problem at large scale and involved three challenging and recently compiled datasets: the Kinetics-600 dataset from Google DeepMind, the AVA dataset from Berkeley and Google, and the Moments in Time dataset from MIT and IBM Research.
2 code implementations • CVPR 2018 • Ranjay Krishna, Ines Chami, Michael Bernstein, Li Fei-Fei
We formulate the cyclic condition between the entities in a relationship by modelling predicates that connect the entities as shifts in attention from one entity to another.
4 code implementations • ICCV 2017 • Ranjay Krishna, Kenji Hata, Frederic Ren, Li Fei-Fei, Juan Carlos Niebles
We also introduce ActivityNet Captions, a large-scale benchmark for dense-captioning events.
3 code implementations • CVPR 2017 • Jonathan Krause, Justin Johnson, Ranjay Krishna, Li Fei-Fei
Recent progress on image captioning has made it possible to generate novel sentences describing images in natural language, but compressing an image into a single sentence can describe visual content in only coarse detail.
no code implementations • 15 Sep 2016 • Kenji Hata, Ranjay Krishna, Li Fei-Fei, Michael S. Bernstein
Microtask crowdsourcing is increasingly critical to the creation of extremely large datasets.
no code implementations • 31 Jul 2016 • Cewu Lu, Ranjay Krishna, Michael Bernstein, Li Fei-Fei
We improve on prior work by leveraging language priors from semantic word embeddings to finetune the likelihood of a predicted relationship.
Ranked #2 on
Scene Graph Generation
on VRD
no code implementations • 23 Feb 2016 • Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A. Shamma, Michael S. Bernstein, Fei-Fei Li
Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering.
no code implementations • 14 Feb 2016 • Ranjay Krishna, Kenji Hata, Stephanie Chen, Joshua Kravitz, David A. Shamma, Li Fei-Fei, Michael S. Bernstein
Microtask crowdsourcing has enabled dataset advances in social science and machine learning, but existing crowdsourcing schemes are too expensive to scale up with the expanding volume of data.
no code implementations • CVPR 2015 • Justin Johnson, Ranjay Krishna, Michael Stark, Li-Jia Li, David Shamma, Michael Bernstein, Li Fei-Fei
We introduce a novel dataset of 5, 000 human-generated scene graphs grounded to images and use this dataset to evaluate our method for image retrieval.