这论文做了个教室安全识别的新基准和轻量模型，计算量只要大模型的十分之一，效果还更好，适合关心AI+教育的人看看。
本论文提出一个混合基准，将生成式CCTV风格视频与真实教室姿态数据结合，用于隐私感知的教室事件识别。作者设计了轻量级运动推理框架，先构建层次化运动学表示，再通过知识蒸馏将大教师模型的能力压缩到单阶学生模型。实验显示，该模型在不到十分之一计算成本下优于更大规模基线，并展现出更强的跨领域运动推理和零样本合成到真实泛化能力。论文将公开基准、代码库和支撑工具。
Robust and Efficient Motion Reasoning for Privacy-Aware Classroom Incident Recognition
Can computer vision help make classrooms safer? In this pilot study, we investigate privacy-aware and computationally efficient classroom incident recognition from CCTV-style observations. This setting remains underexplored, with limited benchmarks and few methods designed for the privacy, efficiency, and generalization demands of real-world deployment. We introduce a novel hybrid benchmark combining generative CCTV-style videos with real-world classroom pose data, and propose a lightweight, but robust motion-reasoning framework motivated by the observation that many incidents differ more in motion direction, speed, acceleration, and intensity than in pose alone. To that end, our method first constructs hierarchical kinematic representations of human actions. Our method then distills hierarchical, multi-order kinematic reasoning from a large teacher into a much smaller single-order student, enabling efficient per-person inference while preserving expressive motion understanding. Experiments show that our model outperforms substantially larger baselines at less than one-tenth of their computational cost, while also demonstrating stronger out-of-domain motion reasoning and zero-shot synthetic-to-real generalization. We will publicly release the benchmark, codebase, and supporting tools to facilitate further research in privacy-aware classroom safety.