Scale-Adaptive Convolutions for Scene Parsing

TLDR: The proposed scale-adaptive convolutions are not only differentiable to learn the convolutional parameters and scale coefficients in an end-to-end way, but also of high parallelizability for the convenience of GPU implementation.

Abstract: Many existing scene parsing methods adopt Convolutional Neural Networks with fixed-size receptive fields, which frequently result in inconsistent predictions of large objects and invisibility of small objects. To tackle this issue, we propose a scale-adaptive convolution to acquire flexiblesize receptive fields during scene parsing. Through adding a new scale regression layer, we can dynamically infer the position-adaptive scale coefficients which are adopted to resize the convolutional patches. Consequently, the receptive fields can be adjusted automatically according to the various sizes of the objects in scene images. Thus, the problems of invisible small objects and inconsistent large-object predictions can be alleviated. Furthermore, our proposed scale-adaptive convolutions are not only differentiable to learn the convolutional parameters and scale coefficients in an end-to-end way, but also of high parallelizability for the convenience of GPU implementation. Additionally, since the new scale regression layers are learned implicitly, any extra training supervision of object sizes is unnecessary. Extensive experiments on Cityscapes and ADE20K datasets well demonstrate the effectiveness of the proposed scaleadaptive convolutions.