Albert Kjøller Jacobsen

Albert Kjøller Jacobsen

MSc. Eng. Human-Centered AI @ Technical University of Denmark

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Future Blog Post: Visual Question Answering with Knowledge-based Semantics

less than 1 minute read

Published:

This blogpost will highlight the main ideas, concepts and findings presented in the Bachelor thesis on Visual Question Answering (VQA) with Knowledge-based Semantics that considers training a VQA model with strong semantic and conceptual understanding by learning towards a latent space spanned by the Conceptnet Numberbatch embeddings on the OK-VQA dataset. The blog post will revisit the model formulation, yet keep an emphasis on the proposed idea of presenting VQA models with strong semantic and conceptual understanding rather than solely focusing on accuracy as the performance metric. In the process, explainability tools are exploited.

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Bachelor Thesis: Visual Question Answering with Knowledge-based Semantics

Published:

This thesis considers an exhaustive state-of-the-art (SOTA) desription of VQA models, theoretical considerations and implementation details for training a VQA model learning towards a semantical- and conceptually strong latent space spanned by the Conceptnet Numberbatch embeddings as well an analysis of model behaviour by exploiting explainability tools.

Recommended citation: Jacobsen, Albert Kjøller; Højbjerg, Phillip Chavarria; Jacobsen, Aron Djurhuus. (2022). "Visual Question Answering with Knowledge-based Semantics." DTU Department of Applied Mathematics and Computer Science . https://findit.dtu.dk/en/catalog/62c6c822d4fccf03d747b3db

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