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AGQA 2.0: An Updated Benchmark for Compositional Spatio-Temporal Reasoning
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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.
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Cited by 2 Pith papers
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Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios
A new attention-based reasoning module combining iterative steps with parallel operation slots improves accuracy on multiple visual question answering benchmarks while staying lightweight and partially interpretable.
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POVQA: Preference-Optimized Video Question Answering with Rationales for Data Efficiency
POVQA reports large F1 gains on a new 239-example video QA dataset after rationale-based fine-tuning, but its own keyframe-only ablation matches the full pooling pipeline, and fine-tuning hurts zero-shot TVQA accuracy.
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