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Zhao Yong Lim

dblp:269/4536 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visual content generation and editing · 50% Multimedia analysis and retrieval · 50%
Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 50% Interaction techniques and input · 50%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Multimedia analysis and retrieval › video summarization
pictorial storyline generation		 0.412020
An AI-empowered Visual Storyline Generator · IJCAI 2020
Visual content generation and editing
video editing		 0.412020
An AI-empowered Visual Storyline Generator · IJCAI 2020
Human-AI interaction › generative AI
AI-generated content		 0.412020
An AI-empowered Visual Storyline Generator · IJCAI 2020
Interaction techniques and input › interactive retrieval
interactive refinement		 0.412020
An AI-empowered Visual Storyline Generator · IJCAI 2020
YearPublicationVenuePosition
2020 An AI-empowered Visual Storyline Generator
abstract
Video editing is currently a highly skill- and time-intensive process. One of the most important tasks in video editing is to compose the visual storyline. This paper outlines Visual Storyline Generator (VSG), an artificial intelligence (AI)-empowered system that automatically generates visual storylines based on a set of images and video footages provided by the user. It is designed to produce engaging and persuasive promotional videos with an easy-to-use interface. In addition, users can be involved in refining the AI-generated visual storylines. The editing results can be used as training data to further improve the AI algorithms in VSG.
Chang Liu 0040, Zhao Yong Lim, Han Yu 0001, Zhiqi Shen 0001, Ian Dixon, Zhanning Gao, Pan Wang 0008, Peiran Ren, Xuansong Xie, Li-Zhen Cui 0001, Chunyan Miao
 IJCAI2