本文针对多车辆实验记录分散的问题，提出了一种基于Apollo-on-Hongqi EV环境的实验框架，利用大型语言模型和强化学习测试辅助组织记录和生成模拟场景。
Existing facial expression recognition (FER) datasets predominantly provide only discrete emotion category labels, failing to capture fine-grained, continuous affective variations. Although some works incorporate Valence-Arousal (VA) annotations, the Dominance (D) dimension remains largely absent. This paper addresses this gap by introducing the first complete, human-verified three-dimensional Valence-Arousal-Dominance (VAD) continuous annotation for the FER2013 dataset. We further propose an orthogonal convolution-based ResNet regression architecture that enforces feature orthogonality to improve VAD value prediction accuracy. Experiments demonstrate that, despite its high annotation difficulty, the D dimension is effectively learnable; orthogonal convolutions significantly enhance predictive performance across all three dimensions—particularly for Dominance. The released VAD-annotated FER2013 dataset and open-source code establish a new benchmark for multidimensional affective computing, enabling more precise, granular emotion analysis in applications such as intelligent education.
Traditional knowledge distillation overlooks the dynamic evolution of student models’ cognitive capacity, limiting knowledge transfer efficiency. To address this, we propose Gompertz-CNN—a novel distillation framework that explicitly models the sigmoidal (S-shaped) learning dynamics of students by integrating the Gompertz growth model into the distillation process for the first time. We further design a phase-aware, time-varying loss weighting mechanism that jointly optimizes Wasserstein-based feature alignment and gradient propagation matching, enabling coordinated alignment at both the feature and backward-propagation levels. The resulting framework is end-to-end trainable and supports multi-objective dynamic distillation. Extensive experiments on CIFAR-10 and CIFAR-100 demonstrate significant improvements over state-of-the-art distillation methods, achieving up to 8% and 4% absolute accuracy gains, respectively. Moreover, Gompertz-CNN exhibits strong effectiveness and robustness across diverse teacher–student architecture pairs.
本文针对多车辆实验记录分散的问题，提出了一种基于Apollo-on-Hongqi EV环境的实验框架，利用大型语言模型和强化学习测试辅助组织记录和生成模拟场景。
Existing facial expression recognition (FER) datasets predominantly provide only discrete emotion category labels, failing to capture fine-grained, continuous affective variations. Although some works incorporate Valence-Arousal (VA) annotations, the Dominance (D) dimension remains largely absent. This paper addresses this gap by introducing the first complete, human-verified three-dimensional Valence-Arousal-Dominance (VAD) continuous annotation for the FER2013 dataset. We further propose an orthogonal convolution-based ResNet regression architecture that enforces feature orthogonality to improve VAD value prediction accuracy. Experiments demonstrate that, despite its high annotation difficulty, the D dimension is effectively learnable; orthogonal convolutions significantly enhance predictive performance across all three dimensions—particularly for Dominance. The released VAD-annotated FER2013 dataset and open-source code establish a new benchmark for multidimensional affective computing, enabling more precise, granular emotion analysis in applications such as intelligent education.
Traditional knowledge distillation overlooks the dynamic evolution of student models’ cognitive capacity, limiting knowledge transfer efficiency. To address this, we propose Gompertz-CNN—a novel distillation framework that explicitly models the sigmoidal (S-shaped) learning dynamics of students by integrating the Gompertz growth model into the distillation process for the first time. We further design a phase-aware, time-varying loss weighting mechanism that jointly optimizes Wasserstein-based feature alignment and gradient propagation matching, enabling coordinated alignment at both the feature and backward-propagation levels. The resulting framework is end-to-end trainable and supports multi-objective dynamic distillation. Extensive experiments on CIFAR-10 and CIFAR-100 demonstrate significant improvements over state-of-the-art distillation methods, achieving up to 8% and 4% absolute accuracy gains, respectively. Moreover, Gompertz-CNN exhibits strong effectiveness and robustness across diverse teacher–student architecture pairs.