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.
@article{arxiv.1909.05690,
title = {In Defense of LSTMs for Addressing Multiple Instance Learning Problems},
author = {Kaili Wang and Jose Oramas and Tinne Tuytelaars},
journal= {arXiv preprint arXiv:1909.05690},
year = {2021}
}