Abstract
Example synthesis is one of the leading methods to tackle
the problem of few-shot learning, where only a small number of samples per class are available. However, current
synthesis approaches only address the scenario of a single
category label per image. In this work, we propose a novel
technique for synthesizing samples with multiple labels for
the (yet unhandled) multi-label few-shot classification scenario. We propose to combine pairs of given examples in
feature space, so that the resulting synthesized feature vectors will correspond to examples whose label sets are obtained through certain set operations on the label sets of
the corresponding input pairs. Thus, our method is capable
of producing a sample containing the intersection, union
or set-difference of labels present in two input samples. As
we show, these set operations generalize to labels unseen
during training. This enables performing augmentation on
examples of novel categories, thus, facilitating multi-label
few-shot classifier learning. We conduct numerous experiments showing promising results for the label-set manipulation capabilities of the proposed approach, both directly
(using the classification and retrieval metrics), and in the
context of performing data augmentation for multi-label
few-shot learning. We propose a benchmark for this new
and challenging task and show that our method compares
favorably to all the common baselines.