Learning Instance Representation Banks for Aerial Scene Classification

TL;DR

This paper introduces an instance representation bank (IRB) framework for aerial scene classification, unifying multiple local descriptors to improve discriminative scene representation and outperform state-of-the-art methods.

Contribution

The paper proposes a novel IRB framework that unifies local semantic descriptors under MIL, enhancing scene representation for aerial image classification.

Findings

Outperforms state-of-the-art methods on three benchmarks.

Effective unification of local descriptors improves scene discrimination.

End-to-end training enhances overall performance.

Abstract

Aerial scenes are more complicated in terms of object distribution and spatial arrangement than natural scenes due to the bird view, and thus remain challenging to learn discriminative scene representation. Recent solutions design \textit{local semantic descriptors} so that region of interests (RoIs) can be properly highlighted. However, each local descriptor has limited description capability and the overall scene representation remains to be refined. In this paper, we solve this problem by designing a novel representation set named \textit{instance representation bank} (IRB), which unifies multiple local descriptors under the multiple instance learning (MIL) formulation. This unified framework is not trivial as all the local semantic descriptors can be aligned to the same scene scheme, enhancing the scene representation capability. Specifically, our IRB learning framework consists of a backbone, an instance representation bank, a semantic fusion module and a scene scheme alignment loss function. All the components are organized in an end-to-end manner. Extensive experiments on three aerial scene benchmarks demonstrate that our proposed method outperforms the state-of-the-art approaches by a large margin.