论文标题
细粒度的动态头进行对象检测
Fine-Grained Dynamic Head for Object Detection
论文作者
论文摘要
特征金字塔网络（FPN）提出了一种非凡的方法，可以通过执行实例级分配来减轻对象表示中的比例差异。然而，这种策略忽略了一个实例中不同子区域的不同特征。为此，我们提出了一个细颗粒的动态头，以有条件地选择每个实例中不同尺度的FPN特征的像素级组合，这进一步释放了多尺度特征表示的能力。此外，我们设计了一个具有新激活函数的空间门，以通过空间稀疏的卷积大大降低计算复杂性。广泛的实验证明了该方法对几个最新检测基准的有效性和效率。代码可从https://github.com/stevengrove/dynamichead获得。
The Feature Pyramid Network (FPN) presents a remarkable approach to alleviate the scale variance in object representation by performing instance-level assignments. Nevertheless, this strategy ignores the distinct characteristics of different sub-regions in an instance. To this end, we propose a fine-grained dynamic head to conditionally select a pixel-level combination of FPN features from different scales for each instance, which further releases the ability of multi-scale feature representation. Moreover, we design a spatial gate with the new activation function to reduce computational complexity dramatically through spatially sparse convolutions. Extensive experiments demonstrate the effectiveness and efficiency of the proposed method on several state-of-the-art detection benchmarks. Code is available at https://github.com/StevenGrove/DynamicHead.
