论文标题
美杜莎：通过注意力多任务学习的通用功能学习
Medusa: Universal Feature Learning via Attentional Multitasking
论文作者
论文摘要
多任务学习（MTL）的最新方法重点是在解码器级别的任务之间建模连接。这会导致任务之间的紧密耦合，如果插入或删除了新任务，则需要重新训练。我们认为MTL是通用通用特征学习（UFL）的垫脚石，它是学习通用功能的能力，可以应用于新任务而无需重新训练。 我们建议美杜莎（Medusa）实现这一目标，以双重注意机制设计任务负责人。共享的功能注意力掩盖每个任务相关的骨干功能，从而可以学习通用表示。同时，一个新颖的多尺度注意力头使网络在做出最终预测时可以更好地结合不同尺度的每任务特征。我们显示了Medusa在UFL中的有效性（+13.18％提高），同时保持MTL性能，并且效率比以前的方法高25％。
Recent approaches to multi-task learning (MTL) have focused on modelling connections between tasks at the decoder level. This leads to a tight coupling between tasks, which need retraining if a new task is inserted or removed. We argue that MTL is a stepping stone towards universal feature learning (UFL), which is the ability to learn generic features that can be applied to new tasks without retraining. We propose Medusa to realize this goal, designing task heads with dual attention mechanisms. The shared feature attention masks relevant backbone features for each task, allowing it to learn a generic representation. Meanwhile, a novel Multi-Scale Attention head allows the network to better combine per-task features from different scales when making the final prediction. We show the effectiveness of Medusa in UFL (+13.18% improvement), while maintaining MTL performance and being 25% more efficient than previous approaches.
