VLDB 2026 Research / reviewers in the wild / expert
Yasunori Mochizuki
dblp:263/0872
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
0009-0007-7915-3251ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 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 · 30% Video understanding and tracking · 30% Information extraction and text analysis · 30% | |
| Computer networks
1 paper |
Internet of things and sensor networks · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Face, body and person analysis
face recognition |
0.9 | 1 | 2025 | Solving Critical Real-World Business Challenges - NEC's Industrial Research Model in the AI Era · ACM Multimedia 2025 |
Computer vision › Video understanding and tracking
video analytics |
0.9 | 1 | 2025 | Solving Critical Real-World Business Challenges - NEC's Industrial Research Model in the AI Era · ACM Multimedia 2025 |
Computer vision › Vision and language
vision-language model |
0.3 | 1 | 2025 | Solving Critical Real-World Business Challenges - NEC's Industrial Research Model in the AI Era · ACM Multimedia 2025 |
Internet of things and sensor networks › wireless sensor network › distributed sensing
distributed acoustic sensing |
0.3 | 1 | 2025 | Solving Critical Real-World Business Challenges - NEC's Industrial Research Model in the AI Era · ACM Multimedia 2025 |
Methods — techniques the papers use, named apart from their topics
multimodal learning · 1.7machine learning · 1.7generative AI · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Solving Critical Real-World Business Challenges - NEC's Industrial Research Model in the AI EraabstractNEC is the leading ICT technology provider in the B-to-B market and is actively integrating cutting-edge technologies into its business solutions to drive innovation, enhance capabilities, and create new value for its customers in a broad spectrum of industrial segments. And the recent business focus of NEC is to support digital transformation of business processes of customer enterprises by leveraging technical capabilities in AI, Cyber Security and Communication. This keynote discusses the specific role of NEC's Research in such a business context by sharing a variety of generative and multimodal AI-related use cases that are aimed at solving critical customer challenges in the real-world. From the multimedia perspective, the topics will include world-leading facial recognition technology for security boost and enhanced customer experience, development of drive-recorder video analytics for insurance adjusters leveraging visual language model (VLM) and medical document generation AI service for genuinely supporting overworked clinical doctors. Meanwhile, distributed acoustic sensing technology using optical fiber cables is opening a new opportunity for infrastructure and incident monitoring solutions after integration with AI and ML algorithms. As the common denominator, our commitment of solving critical customer challenges requires (and justifies) nurturing both world-class excellence in performing academic research and accumulated experience and/or culture of application-oriented technology refinement as well as technology combination to ensure business-ready practicality. Also, being the industrial research organization, we are engaged at the forefront of customer co-creation and co-design that play an indispensable role in pinpointing customer's critical challenges. These expertise and practices are indeed the core ingredients of NEC's Research for creating new business opportunities from the technology innovation approach. Furthermore, we also envision that such an industrial lab model in the Generative AI era will become the driver of a new technology paradigm - industry segment-oriented customizable foundation models and business transforming Agentic AI framework. Yasunori Mochizuki |
ACM Multimedia | 1 |