  Computer vision tasks often have side information available that is helpful
to solve the task. For example, for crowd counting, the camera perspective
(e.g., camera angle and height) gives a clue about the appearance and scale of
people in the scene. While side information has been shown to be useful for
counting systems using traditional hand-crafted features, it has not been fully
utilized in counting systems based on deep learning. In order to incorporate
the available side information, we propose an adaptive convolutional neural
network (ACNN), where the convolutional filter weights adapt to the current
scene context via the side information. In particular, we model the filter
weights as a low-dimensional manifold, parametrized by the side information,
within the high-dimensional space of filter weights. With the help of side
information and adaptive weights, the ACNN can disentangle the variations
related to the side information, and extract discriminative features related to
the current context. Since existing crowd counting datasets do not contain
ground-truth side information, we collect a new dataset with the ground-truth
camera angle and height as the side information. On experiments in crowd
counting, the ACNN improves counting accuracy compared to a plain CNN with a
similar number of parameters. We also apply ACNN to image deconvolution to show
its potential effectiveness on other computer vision applications.
