EthoCLIP：本体增强的视频-语言预训练用于动物行为理解
EthoCLIP: Ontology-Enhanced Video-Language Pretraining for Animal Behavior Understanding
将结构化领域本体（NBO）作为归纳偏置注入视觉-语言对比学习，通过本体感知图模块建模行为层级关系，为数据稀缺的细粒度视频理解提供可复用的语义增强预训练范式。
原文依据 we present EthoCLIP, an ontology-enhanced vision-language contrastive learning framework that embeds ontology semantics through an ontology-aware graph module
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论视觉-语言模型（VLM）在众多领域取得了显著成功，但在动物行为理解方面却明显落后，原因是严重的数据稀缺。标注动物行为视频的收集成本高昂且耗时，需要领域专业知识和受控的观察条件。为应对这一挑战，我们利用结构化领域知识作为来自神经行为本体（NBO）的归纳偏置，该本体提供了专业标注、层级行为结构和全面的语义覆盖。我们构建了AnimalBand，一个与NBO一致的数据集，整合了跨多个物种和行为的74,671个视频，并进行了语义标准化和知识扩展。基于这一资源，我们提出了EthoCLIP，一个本体增强的视觉-语言对比学习框架，通过本体感知图模块嵌入本体语义，以捕捉行为间的层级关系并学习结构化语义依赖。引入本体信息减少了对纯数据驱动学习的依赖，从而缓解了对大规模数据集的需求。大量实验验证了我们的数据集和方法。结果表明，在AnimalBand上预训练的EthoCLIP显著提高了行为识别准确率，并在多个基准上提升了迁移学习性能，证实了本体驱动的语义增强有效缓解了动物行为理解中的数据稀缺问题。我们的数据和代码将在https://github.com/PRIS-CV/AnimalBand发布。
英文原文摘要 1595 字符
Vision-language models (VLMs) have achieved remarkable success across numerous domains, yet they lag significantly in animal behavior understanding due to severe data scarcity. Annotated animal behavior videos are prohibitively expensive and time-consuming to collect, requiring domain expertise and controlled observation conditions. To address this challenge, we leverage structured domain knowledge as an inductive bias from the Neuro Behavior Ontology (NBO), which provides professional annotations, hierarchical behavior structures, and comprehensive semantic coverage. We construct AnimalBand, an NBO-consistent dataset integrating 74,671 videos across multiple species and behaviors with semantic standardization and extended knowledge. Based on this resource, we present EthoCLIP, an ontology-enhanced vision-language contrastive learning framework that embeds ontology semantics through an ontology-aware graph module to capture hierarchical relationships among behaviors and learn structured semantic dependencies. Incorporating ontological information reduces reliance on purely data-driven learning, thereby alleviating needs for large-scale datasets. Extensive experiments validate both our dataset and method. Results demonstrate that EthoCLIP pretrained on AnimalBand substantially improves behavior recognition accuracy and transfer learning performance across diverse benchmarks, confirming that ontology-driven semantic enrichment effectively mitigates data scarcity in animal behavior understanding. Our data and code will be released at https://github.com/PRIS-CV/AnimalBand.