  Multimodal hateful content detection is a challenging task that requires
complex reasoning across visual and textual modalities. Therefore, creating a
meaningful multimodal representation that effectively captures the interplay
between visual and textual features through intermediate fusion is critical.
Conventional fusion techniques are unable to attend to the modality-specific
features effectively. Moreover, most studies exclusively concentrated on
English and overlooked other low-resource languages. This paper proposes a
context-aware attention framework for multimodal hateful content detection and
assesses it for both English and non-English languages. The proposed approach
incorporates an attention layer to meaningfully align the visual and textual
features. This alignment enables selective focus on modality-specific features
before fusing them. We evaluate the proposed approach on two benchmark hateful
meme datasets, viz. MUTE (Bengali code-mixed) and MultiOFF (English).
Evaluation results demonstrate our proposed approach's effectiveness with
F1-scores of $69.7$% and $70.3$% for the MUTE and MultiOFF datasets. The scores
show approximately $2.5$% and $3.2$% performance improvement over the
state-of-the-art systems on these datasets. Our implementation is available at
https://github.com/eftekhar-hossain/Bengali-Hateful-Memes.
