We introduce SaccadeNet, a fast and accurate object detection algorithm. Our model actively attends to informative object keypoints from the center to the corners, and predicts the object bounding boxes from coarse to fine. SaccadeNet runs extremely fast, because these object keypoints are predicted jointly so that we do not need a grouping algorithm to combine them. We extensively evaluate SaccadeNet on PASCAL VOC and MS COCO datasets, which both demonstrates its effectiveness and efficiency
We introduce SaccadeNet, a fast and accurate object detection algorithm. Our model actively attends to informative
object keypoints from the center to the corners, and predicts
the object bounding boxes from coarse to fine. SaccadeNet
runs extremely fast, because these object keypoints are predicted jointly so that we do not need a grouping algorithm
to combine them. We extensively evaluate SaccadeNet on
PASCAL VOC and MS COCO datasets, which both demonstrates its effectiveness and efficiency
SaccadeNet是兼顾效率和精度的目标检测器 它大概是几个注意力模块，一个是Center，一个是corner，一个是Attn-Trans
Center-Attn为SaccadeNet提供了目标中心的第一个视图，并预测目标中心的关键点。它以CNN主干的特征作为输入，预测中心热度图。中心热度图用于估计图像中所有目标的类别和中心位置，在中心热图中通道的数量是类别的数量。图2显示了Center-attn及其输出。在Center-attn中，它包含两个卷积层，这种2-convolutional结构称为head module，它是构建SaccadeNet其它模块的基础组件。
我想在网上找找开源代码，结果只找到你这个帖子，目前还没开源。（难）
注意力机制又是dl的新砖头了，以后要写入教科书了
这能理解成dl在回锅特征工程的理念么？
请问您复现成功了这个算法代码吗？