Reproduce “Multimodal Co-learning: Challenges, Applications with Datasets, Recent Advances and Future Directions” on OpenResearch

About this paper

Anil Rahate, Rahee Walambe, Sheela Ramanna, Ketan Kotecha

This survey from Symbiosis International (Deemed University) develops a comprehensive taxonomy for multimodal co-learning, systematically reviewing techniques that enable AI systems to robustly handle real-world data imperfections such as missing, noisy, or unannotated modalities.

Key insights

• Multimodal co-learning serves as a critical paradigm for building robust multimodal AI systems capable of functioning effectively despite imperfections in real-world data.
• The field of multimodal co-learning previously lacked a dedicated, comprehensive survey and a unifying framework, leading to a fragmented understanding of its objectives and techniques.
• Many existing machine learning techniques (e.g., semi-supervised learning, transfer learning) contribute to co-learning objectives by enabling knowledge transfer from resource-rich to resource-poor modalities.

Summary by alphaXiv.