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Humans are able to estimate the depth of objects in their environment even using only one eye through the use of what are known as monocular cues. In this paper, we aim to integrate human knowledge and human-like reasoning used for monocular depth estimation within deep neural networks. The idea is to support the network in order to help it learn in an explicit and fast way the essential cues for the target task. For this purpose, we investigate the possibility of directly integrating geometric, semantic, and contextual information into the monocular depth estimation process. We propose exploiting an ontology model in a deep learning context to represent the urban environment as a structured set of concepts linked with semantic relationships. Monocular cues information are extracted through reasoning performed on the proposed ontology and are fed together with the RGB image in a multistream way into the deep neural network for depth estimation. Our approach is validated and evaluated on widespread benchmark datasets: KITTI, CityScapes, and AppolloScape. The obtained results show that the proposed method improves upon the state-of-the-art monocular depth estimation deep models and shows promising results regarding cross-evaluation, mainly for unseen driving scenarios. [ABSTRACT FROM AUTHOR]

Recommendation systems or recommender systems (RSs) are very popular in entertainment websites. With the combination of neural networks and collaborative filtering, Neural Collaborative Filtering (NCF) recommendation methods have shown their outperformance in making item suggestions. However, the lack of semantic relationships between objects makes the NCF unable to capture the complex user–item interactions. Moreover, traditional NCF is unable to capture the dynamic user preference over time. To address these issues, in this paper, we propose novel semantic-enhanced NCF models which are applied to movie rating prediction and movie recommendation. Therefore, MovieLens and IMDB datasets are taken into account as case studies. The proposed models are the integration of ontology-like modeling and deep learning for recommendation tasks into two parts:(1) building the semantic knowledge base for movies and (2) building the user behavior analytic model that has semantic knowledge inference on the knowledge base combined with the sequential preference learned from user sessions, input into the NCF module for making predictions or recommendations. Several experiments have been conducted to show their better recommendation performance than the traditional NCF model. [Display omitted] • Building the semantic knowledge base for enhancing deep learning models. • Building the user behavior analytic model with the semantic knowledge base. • Experiments show the proposed models are significantly better than the original NCF. [ABSTRACT FROM AUTHOR]

Because of its wide potential applications, remote sensing (RS) image semantic segmentation has attracted increasing research interest in recent years. Until now, deep semantic segmentation network (DSSN) has achieved a certain degree of success on semantic segmentation of RS imagery and can obviously outperform the traditional methods based on hand-crafted features. As a classic data-driven technique, DSSN can be trained by an end-to-end mechanism and is competent for employing low-level and mid-level cues (i.e., the discriminative image structure) to understand RS images. However, its interpretability and reliability are poor due to the nature weakness of the data-driven deep learning methods. By contrast, human beings have an excellent inference capacity and can reliably interpret RS imagery with the basic RS domain knowledge. Ontological reasoning is an ideal way to imitate and employ the domain knowledge of human beings. However, it is still rarely explored and adopted in the RS domain. As a solution of the aforementioned critical limitation of DSSN, this study proposes a collaboratively boosting framework (CBF) to combine the data-driven deep learning module and knowledge-guided ontology reasoning module in an iterative manner. The deep learning module adopts the DSSN architecture and takes the integration of the original image and inferred channels as the input of the DSSN. In addition, the ontology reasoning module is composed of intra- and extra-taxonomy reasoning. More specifically, the intra-taxonomy reasoning directly corrects misclassifications of the deep learning module based on the domain knowledge, which is the key to improve the classification performance. The extra-taxonomy reasoning aims to generate the inferred channels beyond the current taxonomy to improve the discriminative performance of DSSN in the original RS image space. On the one hand, benefiting from the referred channels from the ontology reasoning module, the deep learning module using the integration of the original image and referred channels can achieve better classification performance than only using the original image. On the other hand, better classification results from the deep learning module further improve the performance of the ontology reasoning module. As a whole, the deep learning and ontology reasoning modules are mutually boosted in the iterations. Extensive experiments on two publicly open RS datasets such as UCM and ISPRS Potsdam show that our proposed CBF can outperform the competitive baselines with a large margin. [ABSTRACT FROM AUTHOR]