  The fully convolutional network (FCN) has achieved tremendous success in
dense visual recognition tasks, such as scene segmentation. The last layer of
FCN is typically a global classifier (1x1 convolution) to recognize each pixel
to a semantic label. We empirically show that this global classifier, ignoring
the intra-class distinction, may lead to sub-optimal results.
  In this work, we present a conditional classifier to replace the traditional
global classifier, where the kernels of the classifier are generated
dynamically conditioned on the input. The main advantages of the new classifier
consist of: (i) it attends on the intra-class distinction, leading to stronger
dense recognition capability; (ii) the conditional classifier is simple and
flexible to be integrated into almost arbitrary FCN architectures to improve
the prediction. Extensive experiments demonstrate that the proposed classifier
performs favourably against the traditional classifier on the FCN architecture.
The framework equipped with the conditional classifier (called CondNet)
achieves new state-of-the-art performances on two datasets. The code and models
are available at https://git.io/CondNet.
