Search Results for author: Ranjay Krishna

Found 21 papers, 8 papers with code

On the Opportunities and Risks of Foundation Models

1 code implementation16 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 Kohd, 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.

Transfer Learning

Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering

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.

Active Learning Object Recognition +3

Determining Question-Answer Plausibility in Crowdsourced Datasets Using Multi-Task Learning

1 code implementation10 Nov 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.

Multi-Task Learning

Conceptual Metaphors Impact Perceptions of Human-AI Collaboration

no code implementations5 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.

Action Genome: Actions as Composition of Spatio-temporal Scene Graphs

1 code implementation15 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.

Action Recognition

Deep Bayesian Active Learning for Multiple Correct Outputs

no code implementations2 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.

Active Learning Image Captioning +3

Scene Graph Prediction with Limited Labels

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.

Knowledge Base Completion Question Answering +2

HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models

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.

Image Generation Unconditional Image Generation

Information Maximizing Visual Question Generation

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.

Question Generation

The ActivityNet Large-Scale Activity Recognition Challenge 2018 Summary

no code implementations11 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.

Activity Recognition

Referring Relationships

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.

Dense-Captioning Events in Videos

1 code implementation 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.

Video Retrieval

A Hierarchical Approach for Generating Descriptive Image Paragraphs

2 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.

Image Captioning Image Paragraph Captioning

A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality

no code implementations15 Sep 2016 Kenji Hata, Ranjay Krishna, Li Fei-Fei, Michael S. Bernstein

Microtask crowdsourcing is increasingly critical to the creation of extremely large datasets.

Visual Relationship Detection with Language Priors

no code implementations31 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.

Content-Based Image Retrieval Visual Relationship Detection +1

Embracing Error to Enable Rapid Crowdsourcing

no code implementations14 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.

General Classification Sentiment Analysis +2

Image Retrieval Using Scene Graphs

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.

Image Retrieval Object Localization

Cannot find the paper you are looking for? You can Submit a new open access paper.