no code implementations • Findings (ACL) 2022 • Minji Seo, YeonJoon Jung, Seungtaek Choi, Seung-won Hwang, Bei Liu
We study event understanding as a critical step towards visual commonsense tasks. Meanwhile, we argue that current object-based event understanding is purely likelihood-based, leading to incorrect event prediction, due to biased correlation between events and objects. We propose to mitigate such biases with do-calculus, proposed in causality research, but overcoming its limited robustness, by an optimized aggregation with association-based prediction. We show the effectiveness of our approach, intrinsically by comparing our generated events with ground-truth event annotation, and extrinsically by downstream commonsense tasks.
no code implementations • 9 Dec 2023 • YeonJoon Jung, Sungsoo Ahn
In this work, we introduce a new graph neural network layer called Triplet Edge Attention (TEA), an edge-aware graph attention layer.