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Vision transformers (ViT) detectors excel in processing natural images. However, when processing remote sensing images (RSIs), ViT methods generally exhibit inferior accuracy compared to approaches based on convolutional neural networks (CNNs). Recently, researchers have proposed various structural optimization strategies to enhance the performance of ViT detectors, but the progress has been insignificant. We contend that the frequent scarcity of RSI samples is the primary cause of this problem, and model modifications alone cannot solve it.

To address this, we introduce a faster RCNN-based approach, termed QAGA-Net, which significantly enhances the performance of ViT detectors in RSI recognition. Initially, we propose a novel quantitative augmentation learning (QAL) strategy to address the sparse data distribution in RSIs. This strategy is integrated as the QAL module, a plug-and-play component active exclusively during the model’s training phase. Subsequently, we enhanced the feature pyramid network (FPN) by introducing two efficient modules: a global attention (GA) module to model long-range feature dependencies and enhance multi-scale information fusion, and an efficient pooling (EP) module to optimize the model’s capability to understand both high and low frequency information. Importantly, QAGA-Net has a compact model size and achieves a balance between computational efficiency and accuracy.

We verified the performance of QAGA-Net by using two different efficient ViT models as the detector’s backbone. Extensive experiments on the NWPU-10 and DIOR20 datasets demonstrate that QAGA-Net achieves superior accuracy compared to 23 other ViT or CNN methods in the literature. Specifically, QAGA-Net shows an increase in mAP by 2.1% or 2.6% on the challenging DIOR20 dataset when compared to the top-ranked CNN or ViT detectors, respectively.

This paper highlights the impact of sparse data distribution on ViT detection performance. To address this, we introduce a fundamentally data-driven approach: the QAL module. Additionally, we introduced two efficient modules to enhance the performance of FPN. More importantly, our strategy has the potential to collaborate with other ViT detectors, as the proposed method does not require any structural modifications to the ViT backbone.

This study aims to examine the determinants of disclosing repetitive year-to-year key audit matters (KAMs) content by auditors for a client and whether repetitive KAMs content is indicative of lower audit effort.

The authors use a sample of publicly listed firms from New Zealand between 2016 and 2020. Multivariate regression models are used to test the determinants of the extent of repetitive content in the KAMs section of the audit report. The authors compare the KAMs disclosed in the current period to those disclosed in prior period(s) to determine the level of recurring items and repetitive textual content.

The authors find evidence of repetitive KAMs content being disclosed at the client level since the reporting requirement was introduced. In multivariate analyses, the authors find that client business risk and auditor reputation are negatively associated with auditors’ reporting of repetitive KAMs. Furthermore, the authors find that auditors exert lower effort on audits for which they report a higher level of repetitive content in KAMs.

The study provides novel findings that contribute to the literature on auditors’ voluntary reporting of KAMs and provide important practical implications for investors and regulators.