VAD-Net: Multidimensional Facial Expression Recognition in Intelligent Education System

A new study presents a Perception CNN (PCNN) designed for facial expression recognition (FER), utilizing five parallel networks to capture subtle facial changes effectively. This approach integrates local features from facial regions such as eyes and mouth with global structural features, enhancing the accuracy of FER systems. Experimental results indicate that PCNN outperforms existing benchmarks like CK+ and JAFFE.

A new paper introduces data taggants, a technique for dataset ownership verification that utilizes harmless targeted data poisoning to subtly alter datasets. This method aims to address the limitations of existing approaches, such as backdoor watermarking, which can harm model performance and lack guarantees against false positives.

A new study introduces the Unified Budget-Aware (UBA) learning rate schedule, designed to optimize training within constrained iteration budgets. This approach addresses the inefficiencies of traditional learning rate schedules, which often rely on heuristic methods and extensive trial-and-error. The UBA schedule has shown superior performance across various architectures and tasks, particularly in budgeted-iteration scenarios.