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AGQA 2.0: An Updated Benchmark for Compositional Spatio-Temporal Reasoning

2022-04-12 22:30:12
Madeleine Grunde-McLaughlin, Ranjay Krishna, Maneesh Agrawala
arXiv_CV
     
Abstract

Prior benchmarks have analyzed models' answers to questions about videos in order to measure visual compositional reasoning. Action Genome Question Answering (AGQA) is one such benchmark. AGQA provides a training/test split with balanced answer distributions to reduce the effect of linguistic biases. However, some biases remain in several AGQA categories. We introduce AGQA 2.0, a version of this benchmark with several improvements, most namely a stricter balancing procedure. We then report results on the updated benchmark for all experiments.

Abstract (translated)

URL

https://arxiv.org/abs/2204.06105

PDF

https://arxiv.org/pdf/2204.06105.pdf

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