Abstract. This paper proposes a novel Dynamic Conditional Convolutional Network (DCCN) to handle conditional few-shot learning, i.e, only
a few training samples are available for each condition. DCCN consists of
dual subnets: DyConvNet contains a dynamic convolutional layer with
a bank of basis filters; CondiNet predicts a set of adaptive weights from
conditional inputs to linearly combine the basis filters. In this manner, a
specific convolutional kernel can be dynamically obtained for each conditional input. The filter bank is shared between all conditions thus only
a low-dimension weight vector needs to be learned. This significantly facilitates the parameter learning across different conditions when training
data are limited. We evaluate DCCN on four tasks which can be formulated as conditional model learning, including specific object counting,
multi-modal image classification, phrase grounding and identity based
face generation. Extensive experiments demonstrate the superiority of
the proposed model in the conditional few-shot learning setting