  Multiple instance learning is qualified for many pattern recognition tasks
with weakly annotated data. The combination of artificial neural network and
multiple instance learning offers an end-to-end solution and has been widely
utilized. However, challenges remain in two-folds. Firstly, current MIL pooling
operators are usually pre-defined and lack flexibility to mine key instances.
Secondly, in current solutions, the bag-level representation can be inaccurate
or inaccessible. To this end, we propose an attention awareness multiple
instance neural network framework in this paper. It consists of an
instance-level classifier, a trainable MIL pooling operator based on spatial
attention and a bag-level classification layer. Exhaustive experiments on a
series of pattern recognition tasks demonstrate that our framework outperforms
many state-of-the-art MIL methods and validates the effectiveness of our
proposed attention MIL pooling operators.
