  This work presents an ensemble system based on various uni-modal and bi-modal
model architectures developed for the SemEval 2022 Task 5: MAMI-Multimedia
Automatic Misogyny Identification. The challenge organizers provide an English
meme dataset to develop and train systems for identifying and classifying
misogynous memes. More precisely, the competition is separated into two
sub-tasks: sub-task A asks for a binary decision as to whether a meme expresses
misogyny, while sub-task B is to classify misogynous memes into the potentially
overlapping sub-categories of stereotype, shaming, objectification, and
violence. For our submission, we implement a new model fusion network and
employ an ensemble learning approach for better performance. With this
structure, we achieve a 0.755 macroaverage F1-score (11th) in sub-task A and a
0.709 weighted-average F1-score (10th) in sub-task B.
