The vast and ever-changing nature of real-world visual data makes it impossible to describe comprehensively through human effort alone, establishing machine learning as a critical tool for handling visual diversity. However, even though deep learning performs remarkably well in many scenarios, the generalization and predictive power of a single model often face challenges when unexpected changes occur in the real-world environment. Evan Shelhamer from UBC and the Vector Institute delivered an insightful lecture at the Department of Biomechatronic Engineering, National Taiwan University on April 9: "Multi-Modeling: If one model is good, then more must be better!" The talk explored how multi-model collaboration can take AI systems to the next level in terms of efficiency, reliability, and environmental adaptability. The lecture covered several inspiring directions, including how to enable models to self-adapt after deployment, how to combine models of different scales to balance performance and computational cost, and how to help models focus more efficiently on truly important information. These strategies collectively point to a core concept: rather than pursuing a single giant model, we should consider how multiple models can collaborate, complementing each other's strengths and weaknesses. This lecture provided new perspectives on the future development of computer vision systems and demonstrated the potential and possibilities of multi-model collaboration in practical applications.