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Despite the potential of multi-modal pre-training to learn highlydiscriminative feature representations from complementary data modalities,current progress is being slowed by the lack of large-scale modality-diversedatasets. By leveraging the natural suitability of E-commerce, where differentmodalities capture complementary semantic information, we contribute alarge-scale multi-modal pre-training dataset M5Product. The dataset comprises 5modalities (image, text, table, video, and audio), covers over 6,000 categoriesand 5,000 attributes, and is 500 larger than the largest publicly availabledataset with a similar number of modalities. Furthermore, M5Product containsincomplete modality pairs and noise while also having a long-taileddistribution, resembling most real-world problems. We further proposeSelf-harmonized ContrAstive LEarning (SCALE), a novel pretraining frameworkthat integrates the different modalities into a unified model through anadaptive feature fusion mechanism, where the importance of each modality islearned directly from the modality embeddings and impacts the inter-modalitycontrastive learning and masked tasks within a multi-modal transformer model.We evaluate the current multi-modal pre-training state-of-the-art approachesand benchmark their ability to learn from unlabeled data when faced with thelarge number of modalities in the M5Product dataset. We conduct extensiveexperiments on four downstream tasks and demonstrate the superiority of ourSCALE model, providing insights into the importance of dataset scale anddiversity.

M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretraining