Cefdet: Cognitive Effectiveness Network Based on Fuzzy Inference for Action Detection

TL;DR

Cefdet introduces a fuzzy inference-based network that simulates human cognition for more accurate action detection, effectively identifying and correcting cognitive abnormalities in detection results.

Contribution

The paper presents a novel cognitive effectiveness network using fuzzy inference to improve action detection accuracy and address cognitive abnormalities.

Findings

Outperforms mainstream algorithms on public datasets

Effectively locates frames with cognitive abnormalities

Enhances detection accuracy through fuzzy-based re-detection

Abstract

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.