Revisiting Visual Grounding

Erik Conser, Kennedy Hahn, Chandler Watson, Melanie Mitchell


Abstract
We revisit a particular visual grounding method: the “Image Retrieval Using Scene Graphs” (IRSG) system of Johnson et al. Our experiments indicate that the system does not effectively use its learned object-relationship models. We also look closely at the IRSG dataset, as well as the widely used Visual Relationship Dataset (VRD) that is adapted from it. We find that these datasets exhibit bias that allows methods that ignore relationships to perform relatively well. We also describe several other problems with the IRSG dataset, and report on experiments using a subset of the dataset in which the biases and other problems are removed. Our studies contribute to a more general effort: that of better understanding what machine-learning methods that combine language and vision actually learn and what popular datasets actually test.
Anthology ID:
W19-1804
Volume:
Proceedings of the Second Workshop on Shortcomings in Vision and Language
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venues:
NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
37–46
Language:
URL:
https://aclanthology.org/W19-1804
DOI:
10.18653/v1/W19-1804
Bibkey:
Cite (ACL):
Erik Conser, Kennedy Hahn, Chandler Watson, and Melanie Mitchell. 2019. Revisiting Visual Grounding. In Proceedings of the Second Workshop on Shortcomings in Vision and Language, pages 37–46, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Revisiting Visual Grounding (Conser et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-1804.pdf
Data
VRD