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2019

NLVR2 Visual Bias Analysis

Suhr, Alane, Artzi, Yoav

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Understand

NLVR2 (Suhr et al., 2019) was designed to be robust for language bias through a data collection process that resulted in each natural language sentence appearing with both true and false labels.

The process did not provide a similar measure of control for visual bias.
This technical report analyzes the potential for visual bias in NLVR2.
We show that some amount of visual bias likely exists.

NLVR2 Visual Bias Analysis · Around

Built on

VisualBERT: A simple and performant baseline for vision and language
Original
Li, L. H., Yatskar, M., Yin, D., Hsieh, C.-J., and Chang, K.-W. (2019) · 1908
Earlier work this paper cites.
A corpus of natural language for visual reasoning
Suhr, A., Lewis, M., Yeh, J., and Artzi, Y. (2017) · 2017
Earlier work this paper cites.

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