Enhancing Multi-Task Learning with Attention Mechanisms

Multi-task learning (MTL) has emerged as a crucial approach for addressing complex computer vision problems in autonomous driving, such as semantic segmentation, object detection, and monocular depth estimation. It reduces computational costs by enabling shared learning across interrelated tasks and gives a good understanding of the visual scene. Nonetheless, optimizing multi-task networks for varied tasks is a considerable problem owing to competing gradients and varying task-specific constraints. We propose in this study an attention-based technique to enhance multi-task network performance in autonomous driving. We dynamically control feature relevance across tasks by including attention mechanisms into convolutional neural networks (CNNs), therefore enabling the model to prioritize pertinent data for every task. Attention helps the model to concentrate on pertinent aspects for every activity, hence enhancing the alignment between the common representations and objectives tailored for each work. By allowing the model to acquire additional discriminative and task-specific features, integration of attention mechanisms helps to increase accuracy and robustness in demanding driving situations.