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

Zhe Luo, Weina Fu, Shuai Liu, Saeed Anwar, Muhammad Saqib, Sambit Bakshi, Khan Muhammad

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 public datasets, validating its effectiveness and superiority.

Primary Subject Area: [Content] Vision and Language

Secondary Subject Area: [Generation] Generative Multimedia

Relevance To Conference: The study "Cefdet: Cognitive Effectiveness Network Based on Fuzzy Inference for Action Detection" significantly contributes to multimedia and multimodal processing, particularly in the context of the ACM Multimedia conference. By introducing a novel Cognitive Effectiveness Network (Cefdet) based on fuzzy inference for action detection, the research addresses critical challenges in accurately interpreting and understanding multimedia content. In the field of multimedia, where innovative approaches to analyzing and interacting with diverse multimedia data are essential, Cefdet offers a unique solution by simulating human cognition processes through fuzzy-driven cognitive effectiveness evaluation. This approach enables Cefdet to identify and address cognitive abnormalities in action detection results, thereby improving the accuracy and reliability of multimedia processing systems. Through extensive experimentation and validation against mainstream algorithms on public datasets, the study demonstrates the effectiveness and superiority of Cefdet, highlighting its potential to advance the state-of-the-art in multimedia processing and facilitate the development of more sophisticated and cognitively aware multimedia applications. Overall, our paper represents a significant step forward in enhancing the capabilities of multimedia systems to interpret and interact with multimedia content effectively.

Submission Number: 3098