  We propose a novel visual context-aware filter generation module which
incorporates contextual information present in images into Convolutional Neural
Networks (CNNs). In contrast to traditional CNNs, we do not employ the same set
of learned convolution filters for all input image instances. Our proposed
input-conditioned convolution filters when combined with techniques inspired by
Multi-instance learning and max-pooling, results in a transformation-invariant
neural network. We investigated the performance of our proposed framework on
three MNIST variations, which covers both rotation and scaling variance, and
achieved 1.13% error on MNIST-rot-12k, 1.12% error on Half-rotated MNIST and
0.68% error on Scaling MNIST, which is significantly better than the
state-of-the-art results. We make use of visualization to further prove the
effectiveness of our visual context-aware convolution filters. Our proposed
visual context-aware convolution filter generation framework can also serve as
a plugin for any CNN based architecture and enhance its modeling capacity.
