To address the increasing need for efficient and accurate content moderation,
we propose an efficient and lightweight deep classification ensemble structure.
Our approach is based on a combination of simple visual features, designed for
high-accuracy classification of violent content with low false positives. Our
ensemble architecture utilizes a set of lightweight models with narrowed-down
color features, and we apply it to both images and videos.
We evaluated our approach using a large dataset of explosion and blast
contents and compared its performance to popular deep learning models such as
ResNet-50. Our evaluation results demonstrate significant improvements in
prediction accuracy, while benefiting from 7.64x faster inference and lower
computation cost.
While our approach is tailored to explosion detection, it can be applied to
other similar content moderation and violence detection use cases as well.
Based on our experiments, we propose a "think small, think many" philosophy in
classification scenarios. We argue that transforming a single, large,
monolithic deep model into a verification-based step model ensemble of multiple
small, simple, and lightweight models with narrowed-down visual features can
possibly lead to predictions with higher accuracy.

基于简化的视觉特征，我们提出了一个高效且轻量级的深度分类集成结构，以解决对高准确率分类具有低误报的暴力内容的需求。通过使用一组轻量级模型进行颜色特征的缩减，并应用于图像和视频，我们评估了该方法并与常用的深度学习模型进行了比较，结果显示了预测准确率的显著提高，同时推广了快速推理和较低的计算成本。虽然我们的方法针对爆炸检测，但也可应用于其他类似的内容管理和暴力检测用例。基于我们的实验，我们提出了 “小思考，多思考” 的分类策略，认为将单一的大型深度模型转化为多个小型、简单且轻量级模型的验证式步骤模型集成可能导致更高准确率的预测。