  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.
