用模糊推理模拟人类认知，提升动作检测的准确性与可靠性
Cefdet: Cognitive Effectiveness Network Based on Fuzzy Inference for Action Detection
- 引入模糊推理构建认知有效性评估模块，模拟人类判断过程
- 在多个公开数据集上优于主流算法，有效定位认知异常帧
- 适合关注动作检测可信度与认知合理性研究的学者
动作检测与理解是多媒体内容生成与交互的基础。然而，现有方法多聚焦于构建复杂的关联推理网络，忽视了检测结果的有效性判断，常产生具有认知异常的检测结果。为此，本文提出基于模糊推理的认知有效性网络（Cefdet），引入“基于认知的检测”概念，模拟人类认知过程。首先，构建模糊驱动的认知有效性评估模块（FCM），结合人体动作特征，模拟认知化检测流程，准确识别出存在认知异常的帧位置；随后，提出基于FCM的模糊认知更新策略（FCS），利用模糊逻辑对认知检测结果进行重检与修正，有效更新异常结果。实验表明，Cefdet在多个公开数据集上显著优于多种主流算法，验证了其有效性与优越性。代码已开源：https://github.com/12sakura/Cefdet。
原文摘要 · Abstract (English)
Action detection and understanding provide the foundation for the generation and interaction of multimedia content. However, existing methods mainly focus on constructing complex relational inference networks, overlooking the judgment of detection effectiveness. Moreover, these methods frequently generate detection results with cognitive abnormalities. To solve the above problems, this study proposes a cognitive effectiveness network based on fuzzy inference (Cefdet), which introduces the concept of "cognition-based detection" to simulate human cognition. First, a fuzzy-driven cognitive effectiveness evaluation module (FCM) is established to introduce fuzzy inference into action detection. FCM is combined with human action features to simulate the cognition-based detection process, which clearly locates the position of frames with cognitive abnormalities. Then, a fuzzy cognitive update strategy (FCS) is proposed based on the FCM, which utilizes fuzzy logic to re-detect the cognition-based detection results and effectively update the results with cognitive abnormalities. Experimental results demonstrate that Cefdet exhibits superior performance against several mainstream algorithms on the public datasets, validating its effectiveness and superiority. Code is available at https://github.com/12sakura/Cefdet.
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