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.
1 code implementation • CVPR 2021 • Eunji Kim, Siwon Kim, Minji Seo, Sungroh Yoon
Automated diagnosis using deep neural networks in chest radiography can help radiologists detect life-threatening diseases.
2 code implementations • TACL 2019 • Jihyeok Kim, Reinald Kim Amplayo, Kyungjae Lee, Sua Sung, Minji Seo, Seung-won Hwang
The performance of text classification has improved tremendously using intelligently engineered neural-based models, especially those injecting categorical metadata as additional information, e. g., using user/product information for sentiment classification.
Ranked #4 on Sentiment Analysis on User and product information (Yelp 2013 (Acc) metric)