See, Act, Adapt: Active Perception for Unsupervised Cross-Domain Visual Adaptation via Personalized VLM-Guided Agent

Researchers introduce Sea² (See, Act, Adapt), a novel approach that improves AI perception models in new environments by using an intelligent pose-control agent rather than retraining the models themselves. The method keeps perception modules frozen and uses a vision-language model as a controller, achieving significant performance improvements of 13-27% across visual tasks without requiring additional training data.