论文标题
自上而下的控制网络多任务学习
Multi-Task Learning by a Top-Down Control Network
论文作者
论文摘要
随着通用视觉系统执行的一系列任务的扩展，在单个网络中准确有效地执行多个任务已成为一个重要且仍然开放的问题。最近的计算机视觉方法通过分支网络或通过特定于任务向量对网络特征映射进行频道调制来解决此问题。我们提出了一种新颖的体系结构，该体系结构使用专用自上而下的控制网络，以取决于所选任务，图像内容和空间位置的方式修改主识别网络中所有单元的激活。我们通过在四个数据集上的替代性最先进的方法来实现明显更好的结果来展示我们计划的有效性。我们在任务选择性方面进一步证明了我们的优势，扩展了任务的数量和解释性。
As the range of tasks performed by a general vision system expands, executing multiple tasks accurately and efficiently in a single network has become an important and still open problem. Recent computer vision approaches address this problem by branching networks, or by a channel-wise modulation of the network feature-maps with task specific vectors. We present a novel architecture that uses a dedicated top-down control network to modify the activation of all the units in the main recognition network in a manner that depends on the selected task, image content, and spatial location. We show the effectiveness of our scheme by achieving significantly better results than alternative state-of-the-art approaches on four datasets. We further demonstrate our advantages in terms of task selectivity, scaling the number of tasks and interpretability.
