Demo Data from EDBT 2026 / reviewers in the wild / expert
Tal Shafir
dblp:167/8028
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0001-9982-500XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
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 |
Face, body and person analysis · 77% Trustworthy machine learning · 23% | |
| Computer graphics and multimedia
1 paper |
Multimedia analysis and retrieval · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Face, body and person analysis
affective computing |
0.7 | 1 | 2023 | Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion · Proc. IEEE 2023 |
Multimedia analysis and retrieval › affective computing
emotion analysis |
0.7 | 1 | 2023 | Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion · Proc. IEEE 2023 |
Methods — techniques the papers use, named apart from their topics
deep learning · 1.3computer vision · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding EmotionabstractThe emergence of artificial emotional intelligence technology is revolutionizing the fields of computers and robotics, allowing for a new level of communication and understanding of human behavior that was once thought impossible. While recent advancements in deep learning have transformed the field of computer vision, automated understanding of evoked or expressed emotions in visual media remains in its infancy. This foundering stems from the absence of a universally accepted definition of "emotion," coupled with the inherently subjective nature of emotions and their intricate nuances. In this article, we provide a comprehensive, multidisciplinary overview of the field of emotion analysis in visual media, drawing on insights from psychology, engineering, and the arts. We begin by exploring the psychological foundations of emotion and the computational principles that underpin the understanding of emotions from images and videos. We then review the latest research and systems within the field, accentuating the most promising approaches. We also discuss the current technological challenges and limitations of emotion analysis, underscoring the necessity for continued investigation and innovation. We contend that this represents a "Holy Grail" research problem in computing and delineate pivotal directions for future inquiry. Finally, we examine the ethical ramifications of emotion-understanding technologies and contemplate their potential societal impacts. Overall, this article endeavors to equip readers with a deeper understanding of the domain of emotion analysis in visual media and to inspire further research and development in this captivating and rapidly evolving field. James Z. Wang 0001, Sicheng Zhao, Chenyan Wu, Reginald B. Adams Jr., Michelle G. Newman, Tal Shafir, Rachelle Tsachor |
Proc. IEEE | 6 |