34 citations · 60 across the 8 of their papers we have counts for
8 papers
Background-aware Moment Detection for Video Moment Retrieval
Minjoon Jung, Youwon Jang, Seongho Choi +3
Video moment retrieval (VMR) identifies a specific moment in an untrimmed video for a given natural language query. This task is prone to suffer the weak alignment problem innate i…
Modal-specific Pseudo Query Generation for Video Corpus Moment Retrieval
Minjoon Jung, Seongho Choi, Joochan Kim +2
Video corpus moment retrieval (VCMR) is the task to retrieve the most relevant video moment from a large video corpus using a natural language query. For narrative videos, e.g., dr…
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…
Mounting Video Metadata on Transformer-based Language Model for Open-ended Video Question Answering
Donggeon Lee, Seongho Choi, Youwon Jang +1
Video question answering has recently received a lot of attention from multimodal video researchers. Most video question answering datasets are usually in the form of multiple-choi…
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…
Multimodal Dual Attention Memory for Video Story Question Answering
Kyung-Min Kim, Seong-Ho Choi, Jin-Hwa Kim +1
We propose a video story question-answering (QA) architecture, Multimodal Dual Attention Memory (MDAM). The key idea is to use a dual attention mechanism with late fusion. MDAM use…
DeepStory: Video Story QA by Deep Embedded Memory Networks
Kyung-Min Kim, Min-Oh Heo, Seong-Ho Choi +1
Question-answering (QA) on video contents is a significant challenge for achieving human-level intelligence as it involves both vision and language in real-world settings. Here we…