1 code implementation • 19 Dec 2022 • Mina Lee, Megha Srivastava, Amelia Hardy, John Thickstun, Esin Durmus, Ashwin Paranjape, Ines Gerard-Ursin, Xiang Lisa Li, Faisal Ladhak, Frieda Rong, Rose E. Wang, Minae Kwon, Joon Sung Park, Hancheng Cao, Tony Lee, Rishi Bommasani, Michael Bernstein, Percy Liang
To evaluate human-LM interaction, we develop a new framework, Human-AI Language-based Interaction Evaluation (HALIE), that defines the components of interactive systems and dimensions to consider when designing evaluation metrics.
1 code implementation • 15 Dec 2022 • Omar Shaikh, Hongxin Zhang, William Held, Michael Bernstein, Diyi Yang
Generating a Chain of Thought (CoT) has been shown to consistently improve large language model (LLM) performance on a wide range of NLP tasks.
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 • 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 • 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.
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 • 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.
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.
no code implementations • 17 Jul 2017 • Ethan Fast, Binbin Chen, Julia Mendelsohn, Jonathan Bassen, Michael Bernstein
Today's conversational agents are restricted to simple standalone commands.
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 • 22 Feb 2016 • Ethan Fast, William McGrath, Pranav Rajpurkar, Michael Bernstein
From smart homes that prepare coffee when we wake, to phones that know not to interrupt us during important conversations, our collective visions of HCI imagine a future in which computers understand a broad range of human behaviors.
2 code implementations • 22 Feb 2016 • Ethan Fast, Binbin Chen, Michael Bernstein
Given a small set of seed words that characterize a category, Empath uses its neural embedding to discover new related terms, then validates the category with a crowd-powered filter.
no code implementations • CVPR 2016 • Yuke Zhu, Oliver Groth, Michael Bernstein, Li Fei-Fei
It enables a new type of QA with visual answers, in addition to textual answers used in previous work.
Multiple-choice Multiple Choice Question Answering (MCQA) +2
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.
12 code implementations • 1 Sep 2014 • Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, Li Fei-Fei
The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images.