Systems and methods for leveraging semantic information for a multi-domain visual agent. Semantic information can be leveraged to obtain a multi-domain visual agent. To train the multi-domain visual agent, questions can be sampled from question templates for domain-specific label spaces to obtain a unified label space. The domain-specific labels from the domain-specific label spaces can be mapped into natural language descriptions (NLD) to obtain mapped NLD. The mapped NLD can be converted into prompts by combining the questions sampled from the unified label space and the annotations. The semantic information can be learned by iteratively generating outputs from tokens extracted from the prompts using a large-language model (LLM). The multi-domain visual agent (MDVA) can be trained using the semantic information.
LEVERAGING SEMANTIC INFORMATION FOR A MULTI-DOMAIN VISUAL AGENT
2025-05-08
Patent
Electronic Resource
English