27 citations · 50 across the 10 of their papers we have counts for
10 papers
SGRAM: Improving Scene Graph Parsing via Abstract Meaning Representation
Woo Suk Choi, Yu-Jung Heo, Byoung-Tak Zhang
Scene graph is structured semantic representation that can be modeled as a form of graph from images and texts. Image-based scene graph generation research has been actively conduc…
Hypergraph Transformer: Weakly-supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering
Yu-Jung Heo, Eun-Sol Kim, Woo Suk Choi +1
Knowledge-based visual question answering (QA) aims to answer a question which requires visually-grounded external knowledge beyond image content itself. Answering complex question…
Toward a Human-Level Video Understanding Intelligence
Yu-Jung Heo, Minsu Lee, Seongho Choi +5
We aim to develop an AI agent that can watch video clips and have a conversation with human about the video story. Developing video understanding intelligence is a significantly ch…
CogME: A Cognition-Inspired Multi-Dimensional Evaluation Metric for Story Understanding
Minjung Shin, Seongho Choi, Yu-Jung Heo +3
We introduce CogME, a cognition-inspired, multi-dimensional evaluation metric designed for AI models focusing on story understanding. CogME is a framework grounded in human thinkin…
DramaQA: Character-Centered Video Story Understanding with Hierarchical QA
Seongho Choi, Kyoung-Woon On, Yu-Jung Heo +4
Despite recent progress on computer vision and natural language processing, developing a machine that can understand video story is still hard to achieve due to the intrinsic diffi…
Cut-Based Graph Learning Networks to Discover Compositional Structure of Sequential Video Data
Kyoung-Woon On, Eun-Sol Kim, Yu-Jung Heo +1
Conventional sequential learning methods such as Recurrent Neural Networks (RNNs) focus on interactions between consecutive inputs, i.e. first-order Markovian dependency. However,…