Internet memes are one of the most viral types of content in social media and are equally used in promoting hate speech. Towards a more broad understanding of memes, this paper describes the MemoSys system submitted in Task 8 of SemEval 2020, which aims to classify the sentiment of Internet memes and provide a minimum description of the type of humor it depicts (sarcastic, humorous, offensive, motivational) and its semantic scale. The solution presented covers four deep model architectures which are based on a joint fusion between the VGG16 pre-trained model for extracting visual information and the canonical BERT model or TF-IDF for text understanding. The system placed 5th of 36 participating systems in the task A, offering promising prospects to the use of transfer learning to approach Internet memes understanding.