Demo Data from EDBT 2026 / reviewers in the wild / expert
Ian Dixon
dblp:266/2835
· DBLP profile ↗
3ranked-venue papers
0as first author
1since 2021 · last 2021
0000-0002-0971-8460ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 3 · 1 since 2021Artificial intelligence and machine learning · 2
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
2 papers |
Visual content generation and editing · 61% Multimedia analysis and retrieval · 30% Multimedia systems and quality of experience · 9% | |
| Human-computer interaction and pervasive computing
1 paper |
Human-AI interaction · 50% Interaction techniques and input · 50% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Multimedia analysis and retrieval › video summarization
pictorial storyline generation |
0.4 | 1 | 2020 | An AI-empowered Visual Storyline Generator · IJCAI 2020 |
Visual content generation and editing
video editing |
0.4 | 1 | 2020 | An AI-empowered Visual Storyline Generator · IJCAI 2020 |
Visual content generation and editing
video generation |
0.4 | 1 | 2020 | Generating Engaging Promotional Videos for E-commerce Platforms (Student Abstract) · AAAI 2020 |
Human-AI interaction › generative AI
AI-generated content |
0.4 | 1 | 2020 | An AI-empowered Visual Storyline Generator · IJCAI 2020 |
Interaction techniques and input › interactive retrieval
interactive refinement |
0.4 | 1 | 2020 | An AI-empowered Visual Storyline Generator · IJCAI 2020 |
Methods — techniques the papers use, named apart from their topics
visual storyline generation · 0.4shot composition · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Enhancing Viewing Experience of Generated Visual Storylines for Promotional VideosabstractVisual storyline generation is the problem of selecting and sequencing a set of visual materials (i.e. images and video clips) to produce a video to elicit certain cognitive or emotional responses from viewers. In this paper, we enhance the viewing experience of generated visual storylines with the Shot Composition, Selection and Plotting (ShotCSP) approach. Designed for generating promotional videos in ecommerce settings, ShotCSP considers three key film-making principles into the visual storyline generation pipeline: a) proximity-aware scene transition, b) sound logic flow, and c) graphic discontinuity. We propose two novel metrics to enhance viewing experience: 1) Semantic Distance, which measures how related a shot is to the product being promoted; and 2) Salient Region Ratio, which estimates attention to product details in a shot. Through large-scale user evaluation involving 1,748 pairwise comparisons against five state-of-the-art approaches, ShotCSP achieves significantly improved viewing experience. It is a promising approach to enable AI generated promotional videos to benefit e-commerce businesses. Chang Liu 0040, Han Yu 0001, Zhiqi Shen 0001, Ian Dixon, Yingxue Yu, Zhanning Gao, Pan Wang 0008, Peiran Ren, Xuansong Xie, Li-Zhen Cui 0001, Chunyan Miao |
ICME | 4 |
| 2020 | Generating Engaging Promotional Videos for E-commerce Platforms (Student Abstract)abstractThere is an emerging trend for sellers to use videos to promote their products on e-commerce platforms such as Taobao.com. Current video production workflow includes the production of visual storyline by human directors. We propose a system to automatically generate visual storyline based on the input set of visual materials (e.g. video clips or still images) and then produce a promotional video. In particular, we propose an algorithm called Shot Composition, Selection and Plotting (ShotCSP), which generates visual storylines leveraging film-making principles to improve viewing experience and perceived persuasiveness. Chang Liu 0040, Han Yu 0001, Zhiqi Shen 0001, Yingxue Yu, Ian Dixon, Zhanning Gao, Pan Wang 0008, Peiran Ren, Xuansong Xie, Chunyan Miao |
AAAI | 6 |
| 2020 | An AI-empowered Visual Storyline GeneratorabstractVideo 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 |
IJCAI | 5 |