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Convolutional layers are a major driving force behind the successes of deeplearning. Pointwise convolution (PWC) is a 1x1 convolutional filter that isprimarily used for parameter reduction. However, the PWC ignores the spatialinformation around the points it is processing. This design is by choice, inorder to reduce the overall parameters and computations. However, wehypothesize that this shortcoming of PWC has a significant impact on thenetwork performance. We propose an alternative design for pointwiseconvolution, which uses spatial information from the input efficiently. Ourdesign significantly improves the performance of the networks withoutsubstantially increasing the number of parameters and computations. Weexperimentally show that our design results in significant improvement in theperformance of the network for classification as well as detection.

CPWC: Contextual Point Wise Convolution for Object Recognition