Demo Data from VLDB 2026 / reviewers in the wild / expert
Hsin-Yu Lin
dblp:02/10842
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1Human-computer interaction and ubiquitous computing · 1Applied, interdisciplinary, general and emerging computing · 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.
| Artificial intelligence
1 paper |
Vision and language · 100% | |
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Ubiquitous computing and smart environments · 77% Collaborative and social computing · 23% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Vision and language
image captioning |
0.4 | 1 | 2019 | Dixit: Interactive Visual Storytelling via Term Manipulation · WWW 2019 |
Visualization and visual analytics
visual storytelling |
0.4 | 1 | 2019 | Dixit: Interactive Visual Storytelling via Term Manipulation · WWW 2019 |
Ubiquitous computing and smart environments
mobile crowdsourcing |
0.3 | 1 | 2017 | An investigation of using mobile and situated crowdsourcing to collect annotated travel activity data in real-word settings · Int. J. Hum. Comput. Stud. 2017 |
Collaborative and social computing › crowdsourcing › location-based crowdsourcing
situated crowdsourcing |
0.1 | 1 | 2017 | An investigation of using mobile and situated crowdsourcing to collect annotated travel activity data in real-word settings · Int. J. Hum. Comput. Stud. 2017 |
Methods — techniques the papers use, named apart from their topics
transformer decoder · 0.8framenet · 0.8LSTM · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Dixit: Interactive Visual Storytelling via Term ManipulationabstractIn this paper, we introduce Dixit, an interactive visual storytelling system that the user interacts with iteratively to compose a short story for a photo sequence. The user initiates the process by uploading a sequence of photos. Dixit first extracts text terms from each photo which describe the objects (e.g., boy, bike) or actions (e.g., sleep) in the photo, and then allows the user to add new terms or remove existing terms. Dixit then generates a short story based on these terms. Behind the scenes, Dixit uses an LSTM-based model trained on image caption data and FrameNet to distill terms from each image, and utilizes a transformer decoder to compose a context-coherent story. Users change images or terms iteratively with Dixit to create the most ideal story. Dixit also allows users to manually edit and rate stories. The proposed procedure opens up possibilities for interpretable and controllable visual storytelling, allowing users to understand the story formation rationale and to intervene in the generation process. Chao-Chun Hsu, Yu-Hua Chen, Zi-Yuan Chen, Hsin-Yu Lin, Ting-Hao 'Kenneth' Huang, Lun-Wei Ku |
WWW | 4 |
| 2017 | An investigation of using mobile and situated crowdsourcing to collect annotated travel activity data in real-word settings
Yung-Ju Chang, Gaurav Paruthi, Hsin-Ying Wu, Hsin-Yu Lin, Mark W. Newman |
Int. J. Hum. Comput. Stud. | 4 |