  Memes are graphics and text overlapped so that together they present concepts
that become dubious if one of them is absent. It is spread mostly on social
media platforms, in the form of jokes, sarcasm, motivating, etc. After the
success of BERT in Natural Language Processing (NLP), researchers inclined to
Visual-Linguistic (VL) multimodal problems like memes classification, image
captioning, Visual Question Answering (VQA), and many more. Unfortunately, many
memes get uploaded each day on social media platforms that need automatic
censoring to curb misinformation and hate. Recently, this issue has attracted
the attention of researchers and practitioners. State-of-the-art methods that
performed significantly on other VL dataset, tends to fail on memes
classification. In this context, this work aims to conduct a comprehensive
study on memes classification, generally on the VL multimodal problems and
cutting edge solutions. We propose a generalized framework for VL problems. We
cover the early and next-generation works on VL problems. Finally, we identify
and articulate several open research issues and challenges. This is the first
study that presents the generalized view of the advanced classification
techniques concerning memes classification to the best of our knowledge. We
believe this study presents a clear road-map for the Machine Learning (ML)
research community to implement and enhance memes classification techniques.
