Few-shot object detection is a recently emerging branch in the field of computer vision. Recent research studies have proposed several effective methods for object detection with few samples. However, their performances are limited when applied to remote sensing images. In this article, we specifically analyze the characteristics of remote sensing images and propose a few-shot fine-tuning network with a shared attention module (SAM) to adapt to detecting remote sensing objects, which have large size variations. In our SAM, multi-attention maps are computed in the base training stage and shared with the feature extractor in the few-shot fine-tuning stage as prior knowledge to help better locate novel class objects with few samples. Moreover, we design a new few-shot fine-tuning stage with a balanced fine-tuning strategy (BFS), which helps in mitigating the severe imbalance between the number of novel class samples and base class samples caused by the few-shot settings to improve the classification accuracy. We have conducted experiments on two remote sensing datasets (NWPU VHR-10 and DIOR), and the excellent results demonstrate that our method makes full use of the advantages of few-shot learning and the characteristics of remote sensing images to enhance the few-shot detection performance.

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With new type steel slag-blast furnace slag-fluorgypsum-based cemented material, P O42.5 cement, commonly used cementation agent in China, mechanical properties and microstructure of tailings solidification bodies are studied. The hydration products and morphology tailings concretion body in 60 days are analyzed by SEM and XRD test, which reveals the tailings cementation mechanism solidifying with different cementitious material. Furthermore, a large number of slender bar-like ettringite crystals and filamentous network-like calcium-silicate-hydrate gels bond firmly each other, which is the most important reason why steel slag-blast furnace slag-fluorgypsum base cemented material has the best tailings cementation mechanical properties.

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