Sarah Pratt

Third Year PhD, University of Washington
spratt3 [at] cs [dot] washington [dot] edu

I am a PhD student working with Ali Farhadi. I am interested in Machine Learning and Computer Vision. My past work is in vision and language. Previously, I worked on the PRIOR team at The Allen Institute for AI under the mentorship of Ani Kembhavi, Luca Weihs, and Mark Yatskar. I completed my undergraduate degree in Applied Mathematics and Computer Science at Brown University in Providence, RI with academic advisor Caroline Klivans.

W3Schools W3Schools W3Schools



What does a platypus look like? Generating customized prompts for zero-shot image classification

2023    [ updated pdf ]    [ arxiv ]    [ code ]

Sarah Pratt, Ian Covert, Rosanne Liu, Ali Farhadi

This work combines open vocabulary models with large language models (LLMs) to create Customized Prompts via Language models (CuPL, pronounced "couple"). In particular, we leverage the knowledge contained in LLMs in order to generate many descriptive sentences that are used to perform zero-shot image classification with open vocabulary models. We find that this straightforward and general approach improves accuracy on a range of zero-shot image classification benchmarks, including over one percentage point gain on ImageNet. Finally, this simple baseline requires no additional training and remains completely zero-shot.


The Introspective Agent: Interdependence of Strategy, Physiology, and Sensing for Embodied Agents

2022    [ arxiv ]    [ code ]

Sarah Pratt, Luca Weihs, Ali Farhadi

While traditional embodied agents manipulate an environment to best achieve a goal, we argue for an introspective agent, which considers its own abilities in the context of its environment. We show that different environments yield vastly different optimal designs, and increasing long-term planning is often far less beneficial than other improvements, such as increased physical ability.


Learning Generalizable Visual Representations via Interactive Gameplay

Oral Presentation at ICLR 2021    [ arxiv ]

Luca Weihs, Ani Kembhavi, Kiana Ehsani, Sarah Pratt, Winson Han, Alvaro Herrasti, Eric Kolve, Dustin Schwenk, Roozbeh Mottaghi, Ali Farhadi


Grounded Situation Recognition

Spotlight at ECCV 2020    [ arxiv ]    [ code ]    [ demo ]

Sarah Pratt, Mark Yatskar , Luca Weihs, Ali Farhadi, Ani Kembhavi

Situation Recognition is the task of recognizing the activity happening in an image, the actors and objects involved in this activity, and the roles they play. Semantic roles describe how objects in the image participate in the activity described by the verb. While situation recognition addresses what is happening in an image, who is playing a part in this and what their roles are, it does not address a critical aspect of visual understanding: where the involved entities lie in the image. We address this shortcoming and present Grounded Situation Recognition (GSR), a task that builds upon situation recognition and requires one to not just identify the situation observed in the image but also visually ground the identified roles within the corresponding image.



Prompt Generation for Zero-Shot Image Classification

Detailing paper "What does a platypus look like? Generating customized prompts for zero-shot image classification" as well as other relavent works.


University of Washington

Third year PhD student
Advisor: Ali Farhadi
September 2020 - Current

Brown University

Bachelor of Science - Honors
Joint Concentration in Computer Science and Applied Mathemetics
September 2014 - May 2018


Teaching Assistant

cse493: Deep Learning
University of Washington
Spring 2018

Head Teaching Assistant

cs022: Discrete Math and Probability
Brown University
Spring 2018

Undergraduate Teaching Assistant

cs015: Introduction to Object Oriented Programming
cs022: Discrete Math and Probability
Brown University
Fall 2016, Spring 2017

Nonprofit tutoring

Yleana Leadership Academy
Summer 2015