  This work introduces pyramidal convolution (PyConv), which is capable of
processing the input at multiple filter scales. PyConv contains a pyramid of
kernels, where each level involves different types of filters with varying size
and depth, which are able to capture different levels of details in the scene.
On top of these improved recognition capabilities, PyConv is also efficient
and, with our formulation, it does not increase the computational cost and
parameters compared to standard convolution. Moreover, it is very flexible and
extensible, providing a large space of potential network architectures for
different applications. PyConv has the potential to impact nearly every
computer vision task and, in this work, we present different architectures
based on PyConv for four main tasks on visual recognition: image
classification, video action classification/recognition, object detection and
semantic image segmentation/parsing. Our approach shows significant
improvements over all these core tasks in comparison with the baselines. For
instance, on image recognition, our 50-layers network outperforms in terms of
recognition performance on ImageNet dataset its counterpart baseline ResNet
with 152 layers, while having 2.39 times less parameters, 2.52 times lower
computational complexity and more than 3 times less layers. On image
segmentation, our novel framework sets a new state-of-the-art on the
challenging ADE20K benchmark for scene parsing. Code is available at:
https://github.com/iduta/pyconv

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing