用可更新的知识库让边缘设备在野外持续识别物种，减少对云端依赖。
Sustainable Intelligence for the Wild: Democratizing Ecological Monitoring via Knowledge-Adaptive Edge Expert Agents
- 将视觉识别与知识推理分离，用动态知识库替代模型参数存储专家经验。
- 在野外场景中实现无需频繁上传数据的持续学习，降低能耗和网络需求。
- 联合生物学家与原住民社区开发，推动负责任的生态监测技术落地。
快速的生物多样性丧失凸显了有效监测的紧迫性，但人工调查仍耗时耗力。尽管设备端人工智能提供了可扩展的替代方案，其在野外的表现常受环境变化影响。现有方法高度依赖云端资源，需持续上传现场数据以重训练模型，这不适用于偏远地区部署，因会消耗有限的电力和网络。为此，本研究提出从模型适应转向知识适应。我们设计了一种架构，将视觉感知与推理分离，结合视觉编码器与动态知识库。通过显式知识库取代隐式将专家知识编码到模型参数中，该方法支持知识可持续性，以结构化形式保存专家见解。通过与生物学家及原住民社区的跨学科合作，本工作推进了伦理AI协同开发，促进负责任且文化敏感的生态系统管理。
原文摘要 · Abstract (English)
Rapid biodiversity loss underscore the urgency of effective monitoring, yet manual surveys remain resource-intensive. While on-device AI offers a scalable alternative, its performance in the wild is often challenged by environmental variability. Current methods rely heavily on cloud resource, which requires continuous uploading of field data for model retraining. This approach is unsuitable for remote deployments because it consumes limited power and network connectivity. To address these constraints, this research proposes a shift from model adaptation to knowledge adaptation. We introduce an architecture that separates visual perception from reasoning, combining a visual encoder with a dynamic knowledge base. We uses an explicit knowledge base to replace implicitly encoding expert knowledge into model parameters. This method also supports knowledge sustainability by preserving expert insights in a structured form. Through cross-disciplinary collaboration with biologists and Indigenous communities, this work advances ethical AI co-development, fostering responsible and culturally informed ecosystem management.
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