  Traditional supervised learning requires ground truth labels for the training
data, whose collection can be difficult in many cases. Recently, crowdsourcing
has established itself as an efficient labeling solution through resorting to
non-expert crowds. To reduce the labeling error effects, one common practice is
to distribute each instance to multiple workers, whereas each worker only
annotates a subset of data, resulting in the {\it sparse annotation}
phenomenon. In this paper, we note that when meeting with class-imbalance,
i.e., when the ground truth labels are {\it class-imbalanced}, the sparse
annotations are prone to be skewly distributed, which thus can severely bias
the learning algorithm. To combat this issue, we propose one self-training
based approach named {\it Self-Crowd} by progressively adding confident
pseudo-annotations and rebalancing the annotation distribution. Specifically,
we propose one distribution aware confidence measure to select confident
pseudo-annotations, which adopts the resampling strategy to oversample the
minority annotations and undersample the majority annotations. On one
real-world crowdsourcing image classification task, we show that the proposed
method yields more balanced annotations throughout training than the
distribution agnostic methods and substantially improves the learning
performance at different annotation sparsity levels.
