  Most of the few-shot learning methods learn to transfer knowledge from
datasets with abundant labeled data (i.e., the base set). From the perspective
of class space on base set, existing methods either focus on utilizing all
classes under a global view by normal pretraining, or pay more attention to
adopt an episodic manner to train meta-tasks within few classes in a local
view. However, the interaction of the two views is rarely explored. As the two
views capture complementary information, we naturally think of the
compatibility of them for achieving further performance gains. Inspired by the
mutual learning paradigm and binocular parallax, we propose a unified
framework, namely Binocular Mutual Learning (BML), which achieves the
compatibility of the global view and the local view through both intra-view and
cross-view modeling. Concretely, the global view learns in the whole class
space to capture rich inter-class relationships. Meanwhile, the local view
learns in the local class space within each episode, focusing on matching
positive pairs correctly. In addition, cross-view mutual interaction further
promotes the collaborative learning and the implicit exploration of useful
knowledge from each other. During meta-test, binocular embeddings are
aggregated together to support decision-making, which greatly improve the
accuracy of classification. Extensive experiments conducted on multiple
benchmarks including cross-domain validation confirm the effectiveness of our
method.
