  Even with the luxury of having abundant data, multi-label classification is
widely known to be a challenging task to address. This work targets the problem
of multi-label meta-learning, where a model learns to predict multiple labels
within a query (e.g., an image) by just observing a few supporting examples. In
doing so, we first propose a benchmark for Few-Shot Learning (FSL) with
multiple labels per sample. Next, we discuss and extend several solutions
specifically designed to address the conventional and single-label FSL, to work
in the multi-label regime. Lastly, we introduce a neural module to estimate the
label count of a given sample by exploiting the relational inference. We will
show empirically the benefit of the label count module, the label propagation
algorithm, and the extensions of conventional FSL methods on three challenging
datasets, namely MS-COCO, iMaterialist, and Open MIC. Overall, our thorough
experiments suggest that the proposed label-propagation algorithm in
conjunction with the neural label count module (NLC) shall be considered as the
method of choice.
