@inproceedings{vath-etal-2021-beyond,
title = "Beyond Accuracy: A Consolidated Tool for Visual Question Answering Benchmarking",
author = {V{\"a}th, Dirk and
Tilli, Pascal and
Vu, Ngoc Thang},
editor = "Adel, Heike and
Shi, Shuming",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-demo.14",
doi = "10.18653/v1/2021.emnlp-demo.14",
pages = "114--123",
abstract = "On the way towards general Visual Question Answering (VQA) systems that are able to answer arbitrary questions, the need arises for evaluation beyond single-metric leaderboards for specific datasets. To this end, we propose a browser-based benchmarking tool for researchers and challenge organizers, with an API for easy integration of new models and datasets to keep up with the fast-changing landscape of VQA. Our tool helps test generalization capabilities of models across multiple datasets, evaluating not just accuracy, but also performance in more realistic real-world scenarios such as robustness to input noise. Additionally, we include metrics that measure biases and uncertainty, to further explain model behavior. Interactive filtering facilitates discovery of problematic behavior, down to the data sample level. As proof of concept, we perform a case study on four models. We find that state-of-the-art VQA models are optimized for specific tasks or datasets, but fail to generalize even to other in-domain test sets, for example they can not recognize text in images. Our metrics allow us to quantify which image and question embeddings provide most robustness to a model. All code s publicly available.",
}
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%0 Conference Proceedings
%T Beyond Accuracy: A Consolidated Tool for Visual Question Answering Benchmarking
%A Väth, Dirk
%A Tilli, Pascal
%A Vu, Ngoc Thang
%Y Adel, Heike
%Y Shi, Shuming
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F vath-etal-2021-beyond
%X On the way towards general Visual Question Answering (VQA) systems that are able to answer arbitrary questions, the need arises for evaluation beyond single-metric leaderboards for specific datasets. To this end, we propose a browser-based benchmarking tool for researchers and challenge organizers, with an API for easy integration of new models and datasets to keep up with the fast-changing landscape of VQA. Our tool helps test generalization capabilities of models across multiple datasets, evaluating not just accuracy, but also performance in more realistic real-world scenarios such as robustness to input noise. Additionally, we include metrics that measure biases and uncertainty, to further explain model behavior. Interactive filtering facilitates discovery of problematic behavior, down to the data sample level. As proof of concept, we perform a case study on four models. We find that state-of-the-art VQA models are optimized for specific tasks or datasets, but fail to generalize even to other in-domain test sets, for example they can not recognize text in images. Our metrics allow us to quantify which image and question embeddings provide most robustness to a model. All code s publicly available.
%R 10.18653/v1/2021.emnlp-demo.14
%U https://aclanthology.org/2021.emnlp-demo.14
%U https://doi.org/10.18653/v1/2021.emnlp-demo.14
%P 114-123
Markdown (Informal)
[Beyond Accuracy: A Consolidated Tool for Visual Question Answering Benchmarking](https://aclanthology.org/2021.emnlp-demo.14) (Väth et al., EMNLP 2021)
ACL