论文标题
重量机制：添加串联连接串联的常数
Weight mechanism: adding a constant in concatenation of series connect
论文作者
论文摘要
这是一个共识，浅层中的特征图与图像属性（例如纹理和形状）更相关，而抽象的语义表示形式存在于深层。同时，在卷积操作的过程中，某些图像信息将丢失。自然，直接方法将它们结合在一起，以通过串联或添加来获得丢失的详细信息。实际上，在特征融合中流动的图像表示无法完全与语义表示相匹配，并且不同层的语义偏差也破坏了信息纯化，从而导致将无用的信息混合到融合层中。因此，至关重要的是缩小融合层之间的间隙并减少融合过程中噪声的影响。在本文中，我们提出了一种名为重量机制的方法，以减少串联连接串联串联串联的特征图之间的差距，并且通过改变u-net中残留连接连接的串联的重量来改善马萨诸塞州构建数据集的0.80％MIOU的结果。具体而言，我们设计了一种名为Fused U-NET的新体系结构，以测试重量机制，并且还获得了0.12％的MIOU改进。
It is a consensus that feature maps in the shallow layer are more related to image attributes such as texture and shape, whereas abstract semantic representation exists in the deep layer. Meanwhile, some image information will be lost in the process of the convolution operation. Naturally, the direct method is combining them together to gain lost detailed information through concatenation or adding. In fact, the image representation flowed in feature fusion can not match with the semantic representation completely, and the semantic deviation in different layers also destroy the information purification, that leads to useless information being mixed into the fusion layers. Therefore, it is crucial to narrow the gap among the fused layers and reduce the impact of noises during fusion. In this paper, we propose a method named weight mechanism to reduce the gap between feature maps in concatenation of series connection, and we get a better result of 0.80% mIoU improvement on Massachusetts building dataset by changing the weight of the concatenation of series connection in residual U-Net. Specifically, we design a new architecture named fused U-Net to test weight mechanism, and it also gains 0.12% mIoU improvement.
