#zero-shot-personalization News & Analysis

Gate-and-Merge: Zero-shot Compositional Personalization of Vision Language Models

Researchers present Gate-and-Merge, a zero-shot framework enabling vision-language models to recognize and compose multiple user-defined concepts without requiring co-occurrence training data. The approach uses lightweight LoRA adapters for individual concepts and employs a gating mechanism to merge them intelligently at inference time, maintaining concept integrity while enabling compositional personalization.