The 2025 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) was held in San Jose from August 6 to 8. The event commenced with three keynote addresses: Prof. Prem Devanbu (University of California, Davis) discussed the reliability of large language models for code generation and offered guidance on when their results can be trusted. Prof. Edward Y. Chang (Stanford University) presented adaptive multi-modal learning as a way to address LLM limitations. Dr. Ed H. Chi (Google DeepMind) spoke on the future of AI-assisted discovery, highlighting systems that enhance rather than replace human expertise.
During the conference, Mahtab Movhhedrad, a member of the Media Communications Lab (MCL), presented the paper “GUSL-Dehaze: A Green U-Shaped Learning Approach to Image Dehazing.” This work introduced GUSL-Dehaze, a physics-based green learning framework for image dehazing that completely avoids deep neural networks. The method begins with a modified Dark Channel Prior for initial dehazing, followed by a U-shaped architecture enabling unsupervised representation learning. Feature-engineering techniques such as Relevant Feature Test (RFT) and Least-Squares Normal Transform (LNT) were employed to keep the model compact and interpretable. The final dehazed image is produced through a transparent supervised learning stage, allowing the method to achieve performance comparable to deep learning approaches while maintaining a low parameter count and mathematical transparency.
The conference also included a panel session, “Learning Beyond Deep Learning (LB-DL) for Multimedia Processing,” chaired by Prof. Ling Guan (Toronto Metropolitan University/Ryerson University) and Prof. C.-C. Jay Kuo (University of Southern California, Director of the MCL Lab). Prof. Kuo discussed emerging paradigms that challenge the dominance of deep learning, emphasizing the growing importance of interpretability, efficiency, and sustainability in shaping the next generation of multimedia research.