  Few-shot classification studies the problem of quickly adapting a deep
learner to understanding novel classes based on few support images. In this
context, recent research efforts have been aimed at designing more and more
complex classifiers that measure similarities between query and support images,
but left the importance of feature embeddings seldom explored. We show that the
reliance on sophisticated classifier is not necessary and a simple classifier
applied directly to improved feature embeddings can outperform state-of-the-art
methods. To this end, we present a new method named \textbf{DCAP} in which we
investigate how one can improve the quality of embeddings by leveraging
\textbf{D}ense \textbf{C}lassification and \textbf{A}ttentive \textbf{P}ooling.
Specifically, we propose to pre-train a learner on base classes with abundant
samples to solve dense classification problem first and then fine-tune the
learner on a bunch of randomly sampled few-shot tasks to adapt it to few-shot
scenerio or the test time scenerio. We suggest to pool feature maps by applying
attentive pooling instead of the widely used global average pooling (GAP) to
prepare embeddings for few-shot classification during meta-finetuning.
Attentive pooling learns to reweight local descriptors, explaining what the
learner is looking for as evidence for decision making. Experiments on two
benchmark datasets show the proposed method to be superior in multiple few-shot
settings while being simpler and more explainable. Code is available at:
\url{https://github.com/Ukeyboard/dcap/}.
