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Understanding why machine learning algorithms may fail is usually the task of the human expert that uses domain knowledge and contextual information to discover systematic shortcomings in either the data or the algorithm. In this paper, we propose a semantic referee, which is able to extract qualitative features of the errors emerging from deep machine learning frameworks and suggest corrections. The semantic referee relies on ontological reasoning about spatial knowledge in order to characterize errors in terms of their spatial relations with the environment. Using semantics, the reasoner interacts with the learning algorithm as a supervisor. In this paper, the proposed method of the interaction between a neural network classifier and a semantic referee shows how to improve the performance of semantic segmentation for satellite imagery data. [ABSTRACT FROM AUTHOR]

The current state of the art for image annotation and image retrieval tasks is obtained through deep neural network multimodal pipelines, which combine an image representation and a text representation into a shared embedding space. In this paper we evaluate the impact of using the Full-Network embedding (FNE) in this setting, replacing the original image representation in four competitive multimodal embedding generation schemes. Unlike the one-layer image embeddings typically used by most approaches, the Full-Network embedding provides a multi-scale discrete representation of images, which results in richer characterisations. Extensive testing is performed on three different datasets comparing the performance of the studied variants and the impact of the FNE on a levelled playground, i.e., under equality of data used, source CNN models and hyper-parameter tuning. The results obtained indicate that the Full-Network embedding is consistently superior to the one-layer embedding. Furthermore, its impact on performance is superior to the improvement stemming from the other variants studied. These results motivate the integration of the Full-Network embedding on any multimodal embedding generation scheme. [ABSTRACT FROM AUTHOR]