  Multimodal deep learning, especially vision-language models, have gained
significant traction in recent years, greatly improving performance on many
downstream tasks, including content moderation and violence detection. However,
standard multimodal approaches often assume consistent modalities between
training and inference, limiting applications in many real-world use cases, as
some modalities may not be available during inference. While existing research
mitigates this problem through reconstructing the missing modalities, they
unavoidably increase unnecessary computational cost, which could be just as
critical, especially for large, deployed infrastructures in industry. To this
end, we propose a novel guidance network that promotes knowledge sharing during
training, taking advantage of the multimodal representations to train better
single-modality models to be used for inference. Real-world experiments in
violence detection shows that our proposed framework trains single-modality
models that significantly outperform traditionally trained counterparts, while
avoiding increases in computational cost for inference.
