EaSe: A Diagnostic Tool for VQA based on Answer Diversity
Shailza Jolly, Sandro Pezzelle, Moin Nabi · 2021
We propose EASE, a simple diagnostic tool for Visual Question Answering (VQA) which quantifies the difficulty of an image, question sample.EASE is based on the pattern of answers provided by multiple annotators to a given question.In particular, it considers two aspects of the answers: (i) their Entropy; (ii) their Semantic content.First, we prove the validity of our diagnostic to identify samples that are easy/hard for state-of-art VQA models.Second, we show that EASE can be successfully used to select the most-informative samples for training/fine-tuning. Crucially, only information that is readily available in any VQA dataset is used to compute its scores. 1