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Media Dynamics Lab
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A lab that uses AI to analyze images, video, and music in order to tackle social problems.
AboutAbout the lab
New "knowledge" from diverse data.
The Media Dynamics Lab analyzes data from diverse perspectives and continues to create "knowledge," the source of innovation for solving social problems.
Beyond proposing original new technologies, the lab aims for their real-world deployment, working to build a new society where AI and humans collaborate.
In addition to general images, music, and video, its research targets a wide range of data, including medical images and social infrastructure data.
The lab incorporates problem-based learning into its research and contributes to society by opening the demonstration systems it builds to the general public.
Key terms
- Deep learning
- A method that uses a computational structure of stacked layers to automatically discover the features of data from many examples
- Recommendation
- Selecting and showing what a user is likely to watch or buy next, based on their preferences and history
- Gaze region
- The part of an image or video that a person is actually looking at
NewsLab news
Research on generating first-person video from a single third-person video accepted to SIGGRAPH Asia 2026
Technology to quantify the atmosphere of a room developed in joint research with Nitori Holdings Co., Ltd.
Student received a Student Encouragement Award at the Meeting on Image Recognition and Understanding (MIRU2026)
Paper on evaluating 3D scenes object by object accepted to IEEE Access
Paper on dataset distillation accepted to ECCV 2026; press release issued by Hokkaido University
ResearchResearch themes
By analyzing data from diverse perspectives, the lab continues to create "knowledge," the source of innovation for solving social problems. Beyond proposing original new technologies, it aims for their real-world deployment and takes on the challenge of realizing a super smart society in which AI and humans collaborate.
*Illustrative imageAdvanced Image and Video Recognition
Preparing the large number of labels needed to train AI is difficult. The lab developed a learning method that transfers knowledge extracted from data that is easy to label, such as computer graphics (CG), to real-world data. It is also working on expressing the features extracted by deep learning in elements that people can understand, such as shape and color.
*Illustrative imageVision-Language Integration
By generating images from input text and searching for similar images and videos, the lab made it possible to search content that has no text labels. It is also working on re-searching that narrows down results through dialogue, and on image editing by text instructions alone.
*Illustrative imageWeb and Social Media Analysis
The lab studies recommendation that works even for new users and items with few purchase histories or ratings, a method for creating highlights from sports broadcast video using social media posts as clues, and music recommendation that can explain why a song was recommended.
*Illustrative imageUnderstanding Tacit Knowledge
Where people look differs from person to person. The lab builds AI that estimates gaze regions by capturing gaze tendencies from a small amount of gaze data, and AI that estimates a person's level of interest in a video from motion information such as gaze, facial expression, and skeletal movement while they watch it.
MembersFaculty
The members of the Media Dynamics Lab consist of faculty, administrative assistants, doctoral, master's, and undergraduate students, and research students.As of 2026
- ProfessorMiki Haseyama
- ProfessorTakahiro Ogawa
- Associate ProfessorRen Togo
- Associate ProfessorKeisuke Maeda
- Assistant ProfessorNaoki Saito
- Assistant ProfessorLi Li
- Assistant ProfessorKeigo Sakurai
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Aperture grille closeup” Selçuk Oral (drumex) / CC BY-SA 3.0 (cropped)
- Advanced Image and Video Recognition: “Cat and Dog Cuddling” SeanT313 / CC BY-SA 4.0 (cropped)
- Vision-Language Integration: “CSIRO ScienceImage 11313 The CSIRO GPU cluster at the data centre” division, CSIRO / CC BY 3.0 (cropped)
- Web and Social Media Analysis: “Offset performing for the September 14, 2024 Big Noon Kickoff broadcast in Madison, Wisconsin 0550” SecretName101 / CC BY 4.0 (cropped)
- Understanding Tacit Knowledge: “Iris of human male” Osmo Lundell / CC BY-SA 4.0 (cropped)
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- Related lab (知能情報学研究室): “Quiet Electric Engine V1 (QUEEN V1) Experiment (GRC-2024-C-06530)” NASA Glenn Research Center / NASA/GRC/Sara Lowthian-Hanna / Public domain (cropped)
- Related lab (情報メディア環境学研究室): “Global illumination” user:Gtanski / Public domain (cropped)
- Related lab (知能ソフトウェア研究室): “Yujing Line expressway and Provincial Highway at midnight” Guanting Chen / CC BY-SA 4.0 (cropped)
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