Different layers of deep convolutional neural networks(CNNs) can encode different-level information. High-layer features always contain more semantic information, and low-layer features contain more detail information. However, low-layer features suffer from the background clutter and semantic ambiguity. During visual recognition, the feature combination of the low-layer and high-level features plays an important role in context modulation. If directly combining the high-layer and low-layer features, the background clutter and semantic ambiguity may be caused due to the introduction of detailed information. In this paper, we propose a general network architecture to concatenate CNN features of different layers in a simple and effective way, called Selective Feature Connection Mechanism (SFCM). Low-level features are selectively linked to high-level features with a feature selector which is generated by high-level features. The proposed connection mechanism can effectively overcome the above-mentioned drawbacks. We demonstrate the effectiveness, superiority, and universal applicability of this method on multiple challenging computer vision tasks, including image classification, scene text detection, and image-to-image translation.
翻译:不同层次的深层进化神经网络(CNN)可以对不同层次的信息进行编码。高层次的特征总是包含更多的语义信息,低层次的特征包含更详细的信息。然而,低层次的特征存在背景的模糊性和语义模糊性。在视觉识别过程中,低层次和高层次特征的特征结合在背景调节中起着重要作用。如果将高层次和低层次的特征直接结合在一起,背景的模糊性和语义模糊性可能会由于引入详细的信息而引起。在本文件中,我们建议建立一个一般的网络结构,以简单有效的方式将不同层次的CNN特征配置成一个简单有效的网络结构,称为有选择的特征连接机制(SFCM)。低层次的特征有选择地与高层次的特征选择器的高层次的特征联系在一起。拟议的连接机制可以有效地克服上述缺陷。我们展示了这一方法在多重具有挑战性的计算机视觉任务上的有效性、优越性和普遍适用性,包括图像分类、现场文本检测和图像到图像的翻译。