Dynamic Relation-Aware Multiple Instance Learning for Few-Shot Learning

Abstract

Leveraging patch-level embedding in few-shot learning is widely studied by recent works. However, a fundamental challenge is that labels are actually assigned at image level, whereas patch-level annotations are missing. To deal with this problem, we observe that it exactly matches the applications of multiple instance learning (MIL) and novelly incorporate multiple instance learning with few-shot learning. Specifically, we propose a dynamic relation-aware multiple instance learning framework that explicitly models the spatial and semantic relation on instances and performs iterative aggregation. Extensive experiments demonstrate that the proposed method achieves competitive results compared with state-of-the-arts methods.