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We introduce CommerceMM - a multimodal model capable of providing a diverseand granular understanding of commerce topics associated to the given piece ofcontent (image, text, image+text), and having the capability to generalize to awide range of tasks, including Multimodal Categorization, Image-Text Retrieval,Query-to-Product Retrieval, Image-to-Product Retrieval, etc. We follow thepre-training + fine-tuning training regime and present 5 effective pre-trainingtasks on image-text pairs. To embrace more common and diverse commerce datawith text-to-multimodal, image-to-multimodal, and multimodal-to-multimodalmapping, we propose another 9 novel cross-modal and cross-pair retrieval tasks,called Omni-Retrieval pre-training. The pre-training is conducted in anefficient manner with only two forward/backward updates for the combined 14tasks. Extensive experiments and analysis show the effectiveness of each task.When combining all pre-training tasks, our model achieves state-of-the-artperformance on 7 commerce-related downstream tasks after fine-tuning.Additionally, we propose a novel approach of modality randomization todynamically adjust our model under different efficiency constraints.

CommerceMM: Large-Scale Commerce MultiModal Representation Learning with Omni Retrieval