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Scene classification is one of the most complex tasks in computer-vision. The accuracy of scene classification is dependent on other subtasks such as object detection and object classification. Accurate results may be accomplished by employing object detection in scene classification since prior information about objects in the image will lead to an easier interpretation of the image content. Machine and transfer learning are widely employed in scene classification achieving optimal performance. Despite the promising performance of existing models in scene classification, there are still major issues. First, the training phase for the models necessitates a large amount of data, which is a difficult and time-consuming task. Furthermore, most models are reliant on data previously seen in the training set, resulting in ineffective models that can only identify samples that are similar to the training set. As a result, few-shot learning has been introduced. Although few attempts have been reported applying few-shot learning to scene classification, they resulted in perfect accuracy. Motivated by these findings, in this paper we implement a novel few-shot learning model—GenericConv—for scene classification that has been evaluated using benchmarked datasets: MiniSun, MiniPlaces, and MIT-Indoor 67 datasets. The experimental results show that the proposed model GenericConv outperforms the other benchmark models on the three datasets, achieving accuracies of 52.16 ± 0.015, 35.86 ± 0.014, and 37.26 ± 0.014 for five-shots on MiniSun, MiniPlaces, and MIT-Indoor 67 datasets, respectively. [ABSTRACT FROM AUTHOR]