1 citations · 1 across the 5 of their papers we have counts for
5 papers
Explain Like I'm 5 or Whatever I Choose: Evaluating the Potential for Interactive Complexity in Language Model Responses
Indu Panigrahi, Tal August
Given the increased use of large language models (LLMs) for reading and seeking information and the varying needs and backgrounds of users, direct manipulation of text can be a val…
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…
Comparing Importance Sampling Based Methods for Mitigating the Effect of Class Imbalance
Indu Panigrahi, Richard Zhu
Most state-of-the-art computer vision models heavily depend on data. However, many datasets exhibit extreme class imbalance which has been shown to negatively impact model performa…
Leveraging Structure from Motion to Localize Inaccessible Bus Stops
Indu Panigrahi, Tom Bu, Christoph Mertz
The detection of hazardous conditions near public transit stations is necessary for ensuring the safety and accessibility of public transit. Smart city infrastructures aim to facil…
Improving Data-Efficient Fossil Segmentation via Model Editing
Indu Panigrahi, Ryan Manzuk, Adam Maloof +1
Most computer vision research focuses on datasets containing thousands of images of commonplace objects. However, many high-impact datasets, such as those in medicine and the geosc…