  In this paper, we proposed a pipeline for inferring the relationship of each
class in support set and a query sample using forget-update module. We first
propose a novel architectural module called "channel vector sequence
construction module", which boosts the performance of
sequence-prediction-model-based few-shot classification methods by collecting
the overall information of all support samples and a query sample. The channel
vector sequence generated by this module is organized in a way that each time
step of the sequence contains the information from the corresponding channel of
all support samples and the query sample to be inferred. Channel vector
sequence is obtained by a convolutional neural network and a fully connected
network, and the spliced channel vector sequence is spliced of the
corresponding channel vectors of support samples and a query sample in the
original channel order. Also, we propose a forget-update module consisting of
stacked forget-update blocks. The forget block modify the original information
with the learned weights and the update block establishes a dense connection
for the model. The proposed pipeline, which consists of channel vector sequence
construction module and forget-update module, can infer the relationship
between the query sample and support samples in few-shot classification
scenario. Experimental results show that the pipeline can achieve
state-of-the-art results on miniImagenet, CUB dataset, and cross-domain
scenario.
