  Multimodal deep learning systems which employ multiple modalities like text,
image, audio, video, etc., are showing better performance in comparison with
individual modalities (i.e., unimodal) systems. Multimodal machine learning
involves multiple aspects: representation, translation, alignment, fusion, and
co-learning. In the current state of multimodal machine learning, the
assumptions are that all modalities are present, aligned, and noiseless during
training and testing time. However, in real-world tasks, typically, it is
observed that one or more modalities are missing, noisy, lacking annotated
data, have unreliable labels, and are scarce in training or testing and or
both. This challenge is addressed by a learning paradigm called multimodal
co-learning. The modeling of a (resource-poor) modality is aided by exploiting
knowledge from another (resource-rich) modality using transfer of knowledge
between modalities, including their representations and predictive models.
Co-learning being an emerging area, there are no dedicated reviews explicitly
focusing on all challenges addressed by co-learning. To that end, in this work,
we provide a comprehensive survey on the emerging area of multimodal
co-learning that has not been explored in its entirety yet. We review
implementations that overcome one or more co-learning challenges without
explicitly considering them as co-learning challenges. We present the
comprehensive taxonomy of multimodal co-learning based on the challenges
addressed by co-learning and associated implementations. The various techniques
employed to include the latest ones are reviewed along with some of the
applications and datasets. Our final goal is to discuss challenges and
perspectives along with the important ideas and directions for future work that
we hope to be beneficial for the entire research community focusing on this
exciting domain.
