The way that information propagates in neural networks is of great importance. In this paper, we propose Path Aggregation Network (PANet) aiming at boosting information flow in proposal-based instance segmentation framework. Specifically, we enhance the entire feature hierarchy with accurate localization signals in lower layers by bottom-up path augmentation, which shortens the information path between lower layers and topmost feature. We present adaptive feature pooling, which links feature grid and all feature levels to make useful information in each feature level propagate directly to following proposal subnetworks. A complementary branch capturing different views for each proposal is created to further improve mask prediction. These improvements are simple to implement, with subtle extra computational overhead. Our PANet reaches the 1st place in the COCO 2017 Challenge Instance Segmentation task and the 2nd place in Object Detection task without large-batch training. It is also state-of-the-art on MVD and Cityscapes.
翻译:信息在神经网络中传播的方式非常重要。 在本文中, 我们提出路径聚合网络( Panet), 目的是在基于提案的示例分割框架中促进信息流动。 具体地说, 我们通过自下而上路径增强, 将低层和顶部特征之间的信息路径缩短, 提高整个特征等级, 通过下层的精确定位信号, 从而缩短下层和顶部特征之间的信息路径。 我们展示适应性特征集合, 将每个特征的网格和所有特征级别连接起来, 使每个特征层面的有用信息直接传播到建议子网络。 为了进一步改进对每个建议的不同观点的预测, 我们创建了一个互补分支。 这些改进是简单化的, 并且具有微妙的计算性间接性。 我们的 PANet 到达CO 2017 挑战事件分割任务的第1 位, 并在没有大规模培训的情况下在目标探测任务中占据第2位 。 它也是关于 MVD 和 Cityscovers 的状态。