从物体层面重新定义正常，提升多物体异常检测效果
Redefining Normal: A Novel Object-Level Approach for Multi-Object Novelty Detection
- 以数据集中最主导物体为正常标准，替代传统图像级定义
- 通过密集特征微调与掩码知识蒸馏，显著提升多物体场景检测能力
- 适合实际应用中存在多个目标的异常检测任务
在异常检测领域，无类别信息下准确识别数据中的离群点是一项重大挑战。现有方法在单物体场景表现良好，但在多物体情况下因聚焦于个体对象而表现不佳。本文提出一种新思路：在训练数据集中以物体层面重新定义‘正常’。不同于传统的图像级视角，我们以数据集中最主导的物体作为正常参考，更贴近真实应用场景。基于此物体级正常定义，我们改进知识蒸馏框架，使学生网络从预训练教师网络中学习。第一项贡献是DeFeND（基于正常数据的密集特征微调），将密集特征微调融入蒸馏过程，利用自监督损失引导教师网络关注物体级特征。第二项是掩码知识蒸馏，学生网络在部分输入被遮蔽的情况下学习，提升从不完整数据中推断和泛化的能力。该方法不仅在单物体异常检测中表现优异，更在多物体场景中显著超越现有方法。代码已开源：https://github.com/SMSD75/Redefining_Normal_ACCV24/tree/main
原文摘要 · Abstract (English)
In the realm of novelty detection, accurately identifying outliers in data without specific class information poses a significant challenge. While current methods excel in single-object scenarios, they struggle with multi-object situations due to their focus on individual objects. Our paper suggests a novel approach: redefining `normal' at the object level in training datasets. Rather than the usual image-level view, we consider the most dominant object in a dataset as the norm, offering a perspective that is more effective for real-world scenarios. Adapting to our object-level definition of `normal', we modify knowledge distillation frameworks, where a student network learns from a pre-trained teacher network. Our first contribution, DeFeND(Dense Feature Fine-tuning on Normal Data), integrates dense feature fine-tuning into the distillation process, allowing the teacher network to focus on object-level features with a self-supervised loss. The second is masked knowledge distillation, where the student network works with partially hidden inputs, honing its ability to deduce and generalize from incomplete data. This approach not only fares well in single-object novelty detection but also considerably surpasses existing methods in multi-object contexts. The implementation is available at: https://github.com/SMSD75/Redefining_Normal_ACCV24/tree/main
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