Fusing Domain-Specific Content from Large Language Models into Knowledge Graphs for Enhanced Zero Shot Object State Classification
arxiv(2024)
摘要
Domain-specific knowledge can significantly contribute to addressing a wide
variety of vision tasks. However, the generation of such knowledge entails
considerable human labor and time costs. This study investigates the potential
of Large Language Models (LLMs) in generating and providing domain-specific
information through semantic embeddings. To achieve this, an LLM is integrated
into a pipeline that utilizes Knowledge Graphs and pre-trained semantic vectors
in the context of the Vision-based Zero-shot Object State Classification task.
We thoroughly examine the behavior of the LLM through an extensive ablation
study. Our findings reveal that the integration of LLM-based embeddings, in
combination with general-purpose pre-trained embeddings, leads to substantial
performance improvements. Drawing insights from this ablation study, we conduct
a comparative analysis against competing models, thereby highlighting the
state-of-the-art performance achieved by the proposed approach.
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