@inproceedings{jolly-etal-2021-ease,
title = "{E}a{S}e: A Diagnostic Tool for {VQA} based on Answer Diversity",
author = "Jolly, Shailza and
Pezzelle, Sandro and
Nabi, Moin",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.192",
doi = "10.18653/v1/2021.naacl-main.192",
pages = "2407--2414",
abstract = "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.",
}
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%0 Conference Proceedings
%T EaSe: A Diagnostic Tool for VQA based on Answer Diversity
%A Jolly, Shailza
%A Pezzelle, Sandro
%A Nabi, Moin
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 jun
%I Association for Computational Linguistics
%C Online
%F jolly-etal-2021-ease
%X 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.
%R 10.18653/v1/2021.naacl-main.192
%U https://aclanthology.org/2021.naacl-main.192
%U https://doi.org/10.18653/v1/2021.naacl-main.192
%P 2407-2414
Markdown (Informal)
[EaSe: A Diagnostic Tool for VQA based on Answer Diversity](https://aclanthology.org/2021.naacl-main.192) (Jolly et al., NAACL 2021)
ACL
- Shailza Jolly, Sandro Pezzelle, and Moin Nabi. 2021. EaSe: A Diagnostic Tool for VQA based on Answer Diversity. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2407–2414, Online. Association for Computational Linguistics.