Visual Question Answering (VQA) is a versatile problem combining major fields like Computer Vision (CV) and Natural Language Processing (NLP) for successfully integrating information from questions and images when generating answers requiring common-sense reasoning and knowledge about the world. The task has received a lot of attention from researchers as it is based on human-like capabilities and could as such potentially function as an augmented intelligent system for e.g. helping visually impaired people, education systems or function as a basis for investigating the amalgamation of the symbolic and subsymbolic Artifial Intelligence (AI) paradigms. As creating a well-performing VQA system is far-from trivial - which is shown through a literature review -, this project proposes a novel approach of incorporating external knowledge in the model pipeline by training towards a knowledge-based semantic target space spanned by the pre-trained ConceptNet Numberbatch embeddings, where it is assumed that the Numberbatch-space is exactly what is needed for answering the image-question problems provided through the dataset. Specifically, the Outside Knowledge VQA (OK-VQA) dataset was utilized through the Multi-Modal Framework (MMF) given by Facebook Research. For solving this exact task, experimentation on fusion modules, external knowledge and top-down attention mechanisms were applied with the e↵ort of determining what specifically results in a model’s answer using a well defined evaluation protocol covering qualitative analysis tools such as explainability methods, as an initial analysis of the OK-VQA dataset raised potential issues that might bias the model behaviour. Whether the proposed approach can be deemed successful or not is something up for discussion, since the applied models performed marginally below what was seen for baseline models presented in the OK-VQA paper, however, still provides semantically and conceptually strong answers that encounters input from both the visual- and textual-modality. However, discussions of what resulted in the observed outcome can essentially be summarized in three pointers: 1) the assumption of the Numberbatch-space capturing all what is required by the OK-VQA-proposed problem does not appear as valid, 2) the objective function - manifested as a triplet loss function - is not capable of fully indicating the performance of the model and 3) the optimization approach through hyperparameter tuning should be extended. Regardless of the results, a great foundation for future work as been set.