制作用于目标检测的GBD网络
Crafting GBD-Net for Object Detection
摘要：
在目标检测中，来自不同大小和分辨率的多个支持区域的视觉线索在对候选框进行分类时是互补的。有效地整合来自这些区域的局部和上下文视觉线索已经成为目标检测中的基本问题。在本文中，我们提出了一种门控双向CNN（GBD-Net），在特征学习和特征提取过程中传递来自不同支持区域的特征之间的消息。这种消息传递可以通过在两个方向上的相邻支持区域之间的卷积来实现，并且可以在各个层中进行。因此，局部和上下文视觉模式可以通过学习它们的非线性关系来验证彼此的存在，并且它们的紧密交互以更复杂的方式建模。它还表明，消息传递并不总是有帮助的，但依赖于个别样本。因此，需要门控函数来控制消息传输，其开或关由来自输入样本的额外视觉证据控制。通过在ImageNet、Pascal VOC 2007和Microsoft COCO三个目标检测数据集上的实验，验证了GBD-Net的有效性。本文还展示了我们赢得2016年ImageNet目标检测挑战赛的方法的细节，并在\url{https://github.com/craftGBD/craftGBD}上提供了源代码。
英文摘要：
The visual cues from multiple support regions of different sizes and resolutions are complementary in classifying a candidate box in object detection. Effective integration of local and contextual visual cues from these regions has become a fundamental problem in object detection. In this paper, we propose a gated bi-directional CNN (GBD-Net) to pass messages among features from different support regions during both feature learning and feature extraction. Such message passing can be implemented through convolution between neighboring support regions in two directions and can be conducted in various layers. Therefore, local and contextual visual patterns can validate the existence of each other by learning their nonlinear relationships and their close interactions are modeled in a more complex way. It is also shown that message passing is not always helpful but dependent on individual samples. Gated functions are therefore needed to control message transmission, whose on-or-offs are controlled by extra visual evidence from the input sample. The effectiveness of GBD-Net is shown through experiments on three object detection datasets, ImageNet, Pascal VOC2007 and Microsoft COCO. This paper also shows the details of our approach in wining the ImageNet object detection challenge of 2016, with source code provided on \url{https://github.com/craftGBD/craftGBD}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
