Vitali Petsiuk — accepted AI-conference papers

2021

Black-Box Explanation of Object Detectors via Saliency Maps

Vitali Petsiuk, Rajiv Jain, Varun Manjunatha, Vlad I. Morariu, Ashutosh Mehra, Vicente Ordonez +1

CVPR 2021poster

We propose D-RISE, a method for generating visual explanations for the predictions of object detectors. Utilizing the proposed similarity metric that accounts for both localization and categorization aspects of object detection allows our method to produce saliency maps that show image areas that mo…

Cited by 148 PDF ↗code ↗Scholar ↗

2020

Why do These Match? Explaining the Behavior of Image Similarity Models

Bryan A. Plummer, Mariya I. Vasileva, Vitali Petsiuk, Kate Saenko, David Forsyth

ECCV 2020poster

Explaining a deep learning model can help users understand its behavior and allow researchers to discern its shortcomings. Recent work has primarily focused on explaining models for tasks like image classification or visual question answering. In this paper, we introduce Salient Attributes for Netwo…

Cited by 19 Source ↗PDF ↗code ↗Scholar ↗