Revisiting multiple instance neural networks

Xinggang Wang, Yongluan Yan, Peng Tang, Xiang Bai, Wenyu Liu

Abstract: Highlights•We revisit the problem of solving MIL using neural networks (MINNs), which are ignored in current MIL research community. Our experiments show that MINNs are very effective and efficient.•We proposed a novel MI-Net which is centered on learning bag representation in the neural networks in an end-to-end way.•Recent deep learning tricks including dropout, deep supervision and residual connections are studied in MINNs. We find deep supervision and residual connections are effective for MIL.•In the experiments, the proposed MINNs achieve state-of-the-art or competitive performance on several MIL benchmarks. Moreover, it is extremely fast for both testing and training, for example, it takes only 0.0003 s to predict a bag and a few seconds to train on MIL datasets on a moderate CPU.

External IDs:dblp:journals/pr/WangYTBL18