  Due to the difficulty of collecting exhaustive multi-label annotations,
multi-label datasets often contain partial labels. We consider an extreme of
this weakly supervised learning problem, called single positive multi-label
learning (SPML), where each multi-label training image has only one positive
label. Traditionally, all unannotated labels are assumed as negative labels in
SPML, which introduces false negative labels and causes model training to be
dominated by assumed negative labels. In this work, we choose to treat all
unannotated labels from an alternative perspective, i.e. acknowledging they are
unknown. Hence, we propose entropy-maximization (EM) loss to attain a special
gradient regime for providing proper supervision signals. Moreover, we propose
asymmetric pseudo-labeling (APL), which adopts asymmetric-tolerance strategies
and a self-paced procedure, to cooperate with EM loss and then provide more
precise supervision. Experiments show that our method significantly improves
performance and achieves state-of-the-art results on all four benchmarks. Code
is available at https://github.com/Correr-Zhou/SPML-AckTheUnknown.
