  Memes have become an ubiquitous social media entity and the processing and
analysis of suchmultimodal data is currently an active area of research. This
paper presents our work on theMemotion Analysis shared task of SemEval 2020,
which involves the sentiment and humoranalysis of memes. We propose a system
which uses different bimodal fusion techniques toleverage the inter-modal
dependency for sentiment and humor classification tasks. Out of all
ourexperiments, the best system improved the baseline with macro F1 scores of
0.357 on SentimentClassification (Task A), 0.510 on Humor Classification (Task
B) and 0.312 on Scales of SemanticClasses (Task C).
