Words Aren’t Enough, Their Order Matters: On the Robustness of Grounding Visual Referring Expressions

Arjun Akula, Spandana Gella, Yaser Al-Onaizan, Song-Chun Zhu, Siva Reddy


Abstract
Visual referring expression recognition is a challenging task that requires natural language understanding in the context of an image. We critically examine RefCOCOg, a standard benchmark for this task, using a human study and show that 83.7% of test instances do not require reasoning on linguistic structure, i.e., words are enough to identify the target object, the word order doesn’t matter. To measure the true progress of existing models, we split the test set into two sets, one which requires reasoning on linguistic structure and the other which doesn’t. Additionally, we create an out-of-distribution dataset Ref-Adv by asking crowdworkers to perturb in-domain examples such that the target object changes. Using these datasets, we empirically show that existing methods fail to exploit linguistic structure and are 12% to 23% lower in performance than the established progress for this task. We also propose two methods, one based on contrastive learning and the other based on multi-task learning, to increase the robustness of ViLBERT, the current state-of-the-art model for this task. Our datasets are publicly available at https://github.com/aws/aws-refcocog-adv.
Anthology ID:
2020.acl-main.586
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6555–6565
Language:
URL:
https://www.aclweb.org/anthology/2020.acl-main.586
DOI:
10.18653/v1/2020.acl-main.586
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-main.586.pdf
Video:
 http://slideslive.com/38929224