ComS2T introduces a complementary spatiotemporal learning system to enable efficient model evolution for data adaptation in urban environments.
The author introduces ComS2T, a complementary spatiotemporal learning system, to address data adaptation challenges in evolving models. By disentangling stable and dynamic neural structures and training prompts based on main observations, ComS2T enables efficient adaptation during testing.