Capsule networks aim to parse images into a hierarchy of objects, parts and
relations. While promising, they remain limited by an inability to learn
effective low level part descriptions. To address this issue we propose a way
to learn primary capsule encoders that detect atomic parts from a single image.
During training we exploit motion as a powerful perceptual cue for part
definition, with an expressive decoder for part generation within a layered
image model with occlusion. Experiments demonstrate robust part discovery in
the presence of multiple objects, cluttered backgrounds, and occlusion. The
part decoder infers the underlying shape masks, effectively filling in occluded
regions of the detected shapes. We evaluate FlowCapsules on unsupervised part
segmentation and unsupervised image classification.

研究了通过利用运动感知来学习原子部件的方式来提高 Capsule 网络的检测性能，实验证明在多个对象、杂乱背景和遮挡的情况下具有稳定的部分发现能力，其图像解码器可以有效地填充检测到的形状的遮挡部分，该方法可以用于无监督部分分割和无监督图像分类。