In visual recognition, the key to the performance improvement of ResNet is the success in establishing the stack of deep sequential convolutional layers using identical mapping by a shortcut connection. It results in multiple paths of data flow under a network and the paths are merged with the equal weights. However, it is questionable whether it is correct to use the fixed and predefined weights at the mapping units of all paths. In this paper, we introduce the active weighted mapping method which infers proper weight values based on the characteristic of input data on the fly. The weight values of each mapping unit are not fixed but changed as the input image is changed, and the most proper weight values for each mapping unit are derived according to the input image. For this purpose, channel-wise information is embedded from both the shortcut connection and convolutional block, and then the fully connected layers are used to estimate the weight values for the mapping units. We train the backbone network and the proposed module alternately for a more stable learning of the proposed method. Results of the extensive experiments show that the proposed method works successfully on the various backbone architectures from ResNet to DenseNet. We also verify the superiority and generality of the proposed method on various datasets in comparison with the baseline.

在视觉识别中，ResNet性能提高的关键是通过快捷连接使用相同的映射成功建立深层顺序卷积层的堆栈。它导致网络下数据流的多个路径，并且路径以相等的权重合并。但是，在所有路径的映射单元上使用固定权重和预定义权重是否正确尚存疑问。在本文中，我们介绍了一种主动加权映射方法，该方法根据输入数据的动态特性推断出适当的权重值。每个映射单元的权重值不是固定的，而是随着输入图像的改变而改变的，并且根据输入图像来推导每个映射单元的最合适的权重值。以此目的，从快捷连接和卷积块中嵌入通道信息，然后使用完全连接的层来估计映射单元的权重值。我们交替训练骨干网和提出的模块，以便更稳定地学习提出的方法。大量实验的结果表明，该方法在从ResNet到DenseNet的各种主干架构上均能成功工作。我们还验证了与基线相比，该方法在各种数据集上的优越性和通用性。大量实验的结果表明，该方法在从ResNet到DenseNet的各种主干架构上均能成功工作。我们还验证了与基线相比，该方法在各种数据集上的优越性和通用性。大量实验的结果表明，该方法在从ResNet到DenseNet的各种主干架构上均能成功工作。我们还验证了与基线相比，该方法在各种数据集上的优越性和通用性。