1 citations · 1 across the 2 of their papers we have counts for
7 papers
Presenting Large Language Models as Companions Affects What Mental Capacities People Attribute to Them
Allison Chen, Sunnie S. Y. Kim, Angel Franyutti +4
How might messages about large language models (LLMs) found in public discourse influence the way people think about and interact with these models? To explore this question, we ra…
Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations
Indu Panigrahi, Sunnie S. Y. Kim, Amna Liaqat +4
Explanations for computer vision models are important tools for interpreting how the underlying models work. However, they are often presented in static formats, which pose challen…
Cleaning and Structuring the Label Space of the iMet Collection 2020
Vivien Nguyen, Sunnie S. Y. Kim
The iMet 2020 dataset is a valuable resource in the space of fine-grained art attribution recognition, but we believe it has yet to reach its true potential. We document the unique…
[Re] Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
Sunnie S. Y. Kim, Sharon Zhang, Nicole Meister +1
Singh et al. (2020) point out the dangers of contextual bias in visual recognition datasets. They propose two methods, CAM-based and feature-split, that better recognize an object…
Information-Theoretic Segmentation by Inpainting Error Maximization
Pedro Savarese, Sunnie S. Y. Kim, Michael Maire +2
We study image segmentation from an information-theoretic perspective, proposing a novel adversarial method that performs unsupervised segmentation by partitioning images into maxi…
Fair Attribute Classification through Latent Space De-biasing
Vikram V. Ramaswamy, Sunnie S. Y. Kim, Olga Russakovsky
Fairness in visual recognition is becoming a prominent and critical topic of discussion as recognition systems are deployed at scale in the real world. Models trained from data in…