14 citations · 14 across the 1 of their papers we have counts for
4 papers
Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark
Vitali Petsiuk, Alexander E. Siemenn, Saisamrit Surbehera +11
We provide a new multi-task benchmark for evaluating text-to-image models. We perform a human evaluation comparing the most common open-source (Stable Diffusion) and commercial (DA…
Why do These Match? Explaining the Behavior of Image Similarity Models
Bryan A. Plummer, Mariya I. Vasileva, Vitali Petsiuk +2
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 fo…
Guided Zoom: Questioning Network Evidence for Fine-grained Classification
Sarah Adel Bargal, Andrea Zunino, Vitali Petsiuk +4
We propose Guided Zoom, an approach that utilizes spatial grounding of a model's decision to make more informed predictions. It does so by making sure the model has "the right reas…
RISE: Randomized Input Sampling for Explanation of Black-box Models
Vitali Petsiuk, Abir Das, Kate Saenko
Deep neural networks are being used increasingly to automate data analysis and decision making, yet their decision-making process is largely unclear and is difficult to explain to…