Overview
The DeepModAI 2025 workshop is set to take place on November 24, 2025, in Okinawa, Japan, as part of the ICONIP 2025 conference. This workshop aims to unite academic researchers and industry experts to tackle significant challenges in deep multimodal learning. The event emphasizes advanced deep learning methods that facilitate the learning of transferable latent representations across various modalities, moving beyond traditional unimodal approaches.
Background & Relevance
Deep multimodal learning is an evolving field that integrates information from multiple sources or modalities, such as text, images, and audio. This integration is crucial for developing systems that can understand and process complex data in a more human-like manner. The significance of this workshop lies in its focus on innovative techniques that enhance the adaptability and efficiency of machine learning models in real-world applications, including health monitoring, autonomous systems, and environmental modeling.
Key Details
- Extended Submission Deadline: October 20, 2025
- Workshop Date: November 24, 2025
- Location: Okinawa, Japan
- Topics of Interest:
- Multi-view and multi-modal architecture design
- Cross-modal alignment and translation
- Attention mechanisms for dynamic modality fusion
- Diversity-aware and ensemble learning methods
- Explainable and collaborative multimodal frameworks
- Adaptability to dynamic, incomplete, or context-dependent data
- Scalable deployment and computational efficiency
- Submission Types:
- Extended abstracts (2 pages)
- Regular papers (any length)
- Presentation Format: Oral or poster presentations; online options available for select submissions
- Website for More Information: DeepModAI 2025
Eligibility & Participation
The workshop invites contributions from researchers, practitioners, and students in the field of machine learning and multimodal data analysis. It is particularly relevant for those engaged in cutting-edge research or applications of deep learning techniques.
Submission or Application Guidelines
Participants are encouraged to submit their extended abstracts or regular papers to a preprint repository such as arXiv or Jxiv before submitting to the workshop. Accepted papers will be showcased at the workshop and published on the workshop’s website. For those interested in online presentations, it is advised to reach out to the organizing committee in advance.
Additional Context / Real-World Relevance
The integration of multimodal data is becoming increasingly important in various sectors, including healthcare, robotics, and environmental science. By fostering discussions and collaborations at this workshop, participants can contribute to advancing the state of the art in deep learning, ultimately leading to more robust and versatile AI systems.
Conclusion
The DeepModAI 2025 workshop presents a valuable opportunity for researchers and practitioners to share their insights and findings in the field of multimodal deep learning. Interested individuals are encouraged to apply and contribute to this important dialogue in Okinawa. For further inquiries, please contact the organizing committee at deepmodai@sciencesconf.org.
Category: CFP & Deadlines
Tags: deep learning, multimodal ai, workshop, ICONIP 2025, Okinawa, machine learning, health monitoring, robotics, environmental modeling
