Facial emotion recognition achieves great success with the help of large neural models but also fails to be applied in practical situations due to the large model size of neural methods. To bridge this gap, in this paper, we combine two mainstream model compression methods (pruning and quantization) together, and propose a pruning-then-quantization framework to compress the neural models for facial emotion recognition tasks. Experiments on three datasets show that our model could achieve a high model compression ratio and maintain the model’s high performance well. Besides, We analyze the layer-wise compression performance of our proposed framework to explore its effect and adaptability in fine-grained modules.

Autonomous driving and self-driving vehicles have become the most popular selection for customers for their convenience. Vehicle angle prediction is one of the most prevalent topics in the autonomous driving industry, that is, realizing real-time vehicle angle prediction. However, existing methods of vehicle angle prediction utilize only single-modal data to achieve model prediction, such as images captured by the camera, which limits the performance and efficiency of the prediction system. In this paper, we present Emma, a novel vehicle angle prediction strategy that achieves multi-modal prediction and is more efficient. Specifically, Emma exploits both images and inertial measurement unit (IMU) signals with a fusion network for multi-modal data fusion and vehicle angle prediction. Moreover, we design and implement a few-shot learning module in Emma for fast domain adaptation to varied scenarios (e.g., different vehicle models). Evaluation results demonstrate that Emma achieves overall 97.5% accuracy in predicting three vehicle angle parameters (yaw, pitch, and roll), which outperforms traditional single-modalities by approximately 16.7%–36.8%. Additionally, the few-shot learning module presents promising adaptive ability and shows overall 79.8% and 88.3% accuracy in 5-shot and 10-shot settings, respectively. Finally, empirical results show that Emma reduces energy consumption by 39.7% when running on the Arduino UNO board.