Knowledge-Enhanced Vision-Language Models for Accurate Image Understanding
Keywords:
vision-language models, knowledge enhancement, image understanding, retrieval augmentation, multimodal systems, governance, robustness, fairness, deployment sustainabilityAbstract
Vision-language models have become central to image understanding because they align visual perception with natural language reasoning, yet their accuracy remains uneven in domains where factual precision, spatial relations, and context-sensitive meaning are essential. This paper examines knowledge-enhanced vision-language models as socio-technical systems rather than isolated model architectures. It argues that accurate image understanding depends on the combined design of multimodal encoders, structured and unstructured knowledge sources, retrieval infrastructure, adaptation mechanisms, governance instruments, and evaluation regimes. The analysis first reviews architectural foundations, including transformer-based fusion, contrastive pretraining, generative vision-language modeling, and parameter-efficient adaptation. It then considers how external knowledge, knowledge graphs, retrieval augmentation, and instruction tuning can improve grounding while introducing latency, maintenance, and provenance trade-offs. The paper also explores deployment sustainability, robustness, fairness, and policy implications, emphasizing that knowledge enhancement is not merely a technical upgrade but an institutional commitment. Through cross-domain comparisons in healthcare, industrial inspection, public administration, and embodied systems, the discussion shows that accurate image understanding requires traceable evidence, calibrated uncertainty, accessible documentation, and continuous monitoring. The conclusion contends that the most reliable knowledge-enhanced systems will be those that treat knowledge as a governed infrastructure, not as a static database attached to a model. Such systems must balance accuracy with accountability, performance with sustainability, and automation with human oversight. The paper contributes a system-level research agenda for designing vision-language models that are accurate, robust, fair, and governable across high-stakes visual environments.
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