Training Transitive and Commutative Multimodal Transformers with LoReTTa

Manuel Tran, Amal Lahiani, Yashin Dicente Cid, Fabian J. Theis, Tingying Peng, Eldad Klaiman
Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Computer Vision and Pattern Recognition (cs.CV)
2023-05-22 16:00:00
Training multimodal foundation models is challenging due to the limited availability of multimodal datasets. While many public datasets pair images with text, few combine images with audio or text with audio. Even rarer are datasets that align all three modalities at once. Critical domains such as healthcare, infrastructure, or transportation are particularly affected by missing modalities. This makes it difficult to integrate all modalities into a large pre-trained neural network that can be used out-of-the-box or fine-tuned for different downstream tasks. We introduce LoReTTa (Linking mOdalities with a tRansitive and commutativE pre-Training sTrAtegy) to address this understudied problem. Our self-supervised framework unifies two major language learning paradigms with the rules of commutativity and transitivity. This allows us to transition within and between modalities. As a result, our pre-trained models are better at exploring the true underlying joint probability distribution. For example, LoReTTa can model the relation A -> C with A -> B -> C. Given a dataset containing only the disjoint combinations (A, B) and (B, C), we show that a transformer pre-trained with LoReTTa can handle any mixture of modalities at inference time, including the never-seen pair (A, C) and the triplet (A, B, C). We extensively evaluate our approach on a synthetic, medical, and reinforcement learning dataset. Across different domains, our universal multimodal transformer consistently outperforms strong baselines such as GPT, BERT, and CLIP on tasks involving the missing modality combinations.
PDF: Training Transitive and Commutative Multimodal Transformers with LoReTTa.pdf
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