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.- Anthology ID:
- 2021.naacl-main.192
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2407–2414
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.192
- DOI:
- 10.18653/v1/2021.naacl-main.192
- Cite (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.
- Cite (Informal):
- EaSe: A Diagnostic Tool for VQA based on Answer Diversity (Jolly et al., NAACL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2021.naacl-main.192.pdf
- Code
- shailzajolly/ease
- Data
- Visual Question Answering, Visual Question Answering v2.0