This research introduces a novel approach for identifying specific individuals based on natural language descriptions, addressing critical limitations in existing models.

• Develops a more robust framework for person-specific detection that goes beyond simple one-to-one referring
• Enhances model capabilities to handle complex descriptions and identify individuals in crowded environments
• Introduces techniques to improve security applications by enabling more accurate identification of persons of interest
• Establishes more comprehensive benchmarks to drive future advancements

This research has significant implications for security systems, enabling more natural and effective person identification for surveillance and access control applications.

Referring to Any Person