Abstract
We introduce CARETS, a systematic test suite to measure consistency and robustness of modern VQA models through a series of six fine-grained capability tests. In contrast to existing VQA test sets, CARETS features balanced question generation to create pairs of instances to test models, with each pair focusing on a specific capability such as rephrasing, logical symmetry or image obfuscation. We evaluate six modern VQA systems on CARETS and identify several actionable weaknesses in model comprehension, especially with concepts such as negation, disjunction, or hypernym invariance. Interestingly, even the most sophisticated models are sensitive to aspects such as swapping the order of terms in a conjunction or varying the number of answer choices mentioned in the question. We release CARETS to be used as an extensible tool for evaluating multi-modal model robustness.- Anthology ID:
- 2022.acl-long.443
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6392–6405
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.443
- DOI:
- 10.18653/v1/2022.acl-long.443
- Cite (ACL):
- Carlos E. Jimenez, Olga Russakovsky, and Karthik Narasimhan. 2022. CARETS: A Consistency And Robustness Evaluative Test Suite for VQA. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6392–6405, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- CARETS: A Consistency And Robustness Evaluative Test Suite for VQA (Jimenez et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.acl-long.443.pdf
- Code
- princeton-nlp/carets
- Data
- GQA, Visual Genome, Visual Question Answering