  LSTMs have a proven track record in analyzing sequential data. But what about
unordered instance bags, as found under a Multiple Instance Learning (MIL)
setting? While not often used for this, we show LSTMs excell under this setting
too. In addition, we show thatLSTMs are capable of indirectly capturing
instance-level information us-ing only bag-level annotations. Thus, they can be
used to learn instance-level models in a weakly supervised manner. Our
empirical evaluation on both simplified (MNIST) and realistic (Lookbook and
Histopathology) datasets shows that LSTMs are competitive with or even surpass
state-of-the-art methods specially designed for handling specific MIL problems.
Moreover, we show that their performance on instance-level prediction is close
to that of fully-supervised methods.
