We propose the autofocus convolutional layer for semantic segmentation with
the objective of enhancing the capabilities of neural networks for multi-scale
processing. Autofocus layers adaptively change the size of the effective
receptive field based on the processed context to generate more powerful
features. This is achieved by parallelising multiple convolutional layers with
different dilation rates, combined by an attention mechanism that learns to
focus on the optimal scales driven by context. By sharing the weights of the
parallel convolutions we make the network scale-invariant, with only a modest
increase in the number of parameters. The proposed autofocus layer can be
easily integrated into existing networks to improve a model's representational
power. We evaluate our models on the challenging tasks of multi-organ
segmentation in pelvic CT and brain tumor segmentation in MRI and achieve very
promising performance.

本研究提出自适应对焦卷积层用于语义分割，通过并行合并多个不同膨胀率的卷积层及注意力机制，该卷积层可以根据处理后的上下文自适应地改变有效感受野的大小，提高网络的多尺度处理能力和特征提取能力，且可以轻松地集成到已有的网络中。我们在盆腔 CT 和脑部 MRI 分割等具有挑战性的任务上评估了该方法，获得了非常有希望的性能表现。