论文标题
视觉识别的漏斗激活
Funnel Activation for Visual Recognition
论文作者
论文摘要
我们提出了一种简单但有效的漏斗激活，用于图像识别任务（称为漏斗激活（FRELU）），该任务通过添加可忽略不计的空间条件的开销，将Relu和Prelu扩展到2D激活。 relu和prelu的形式分别为y = max（x，0）和y = max（x，px），而frelu的形式为y = max（x，t（x））的形式，其中t（x）是2D空间条件。此外，空间条件以简单的方式实现了像素的建模能力，并通过常规卷积捕获复杂的视觉布局。我们对成像网，可可检测和语义分割任务进行实验，在视觉识别任务中显示出弗雷鲁的巨大改善和鲁棒性。代码可在https://github.com/megvii-model/funnelact上找到。
We present a conceptually simple but effective funnel activation for image recognition tasks, called Funnel activation (FReLU), that extends ReLU and PReLU to a 2D activation by adding a negligible overhead of spatial condition. The forms of ReLU and PReLU are y = max(x, 0) and y = max(x, px), respectively, while FReLU is in the form of y = max(x,T(x)), where T(x) is the 2D spatial condition. Moreover, the spatial condition achieves a pixel-wise modeling capacity in a simple way, capturing complicated visual layouts with regular convolutions. We conduct experiments on ImageNet, COCO detection, and semantic segmentation tasks, showing great improvements and robustness of FReLU in the visual recognition tasks. Code is available at https://github.com/megvii-model/FunnelAct.
