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

• URL: http://arxiv.org/abs/2202.07247v1
• Date: Tue, 15 Feb 2022 08:23:59 GMT
• Title: CommerceMM: Large-Scale Commerce MultiModal Representation Learning with Omni Retrieval
• Authors: Licheng Yu, Jun Chen, Animesh Sinha, Mengjiao MJ Wang, Hugo Chen, Tamara L. Berg, Ning Zhang
• Abstract summary: CommerceMM is a multimodal model capable of providing a diverse and granular understanding of commerce topics associated to a piece of content. We propose another 9 novel cross-modal and cross-pair retrieval tasks, called Omni-Retrieval pre-training. Our model achieves state-of-the-art performance on 7 commerce-related downstream tasks after fine-tuning.
• Score: 30.607369837039904
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: We introduce CommerceMM - a multimodal model capable of providing a diverse and granular understanding of commerce topics associated to the given piece of content (image, text, image+text), and having the capability to generalize to a wide range of tasks, including Multimodal Categorization, Image-Text Retrieval, Query-to-Product Retrieval, Image-to-Product Retrieval, etc. We follow the pre-training + fine-tuning training regime and present 5 effective pre-training tasks on image-text pairs. To embrace more common and diverse commerce data with text-to-multimodal, image-to-multimodal, and multimodal-to-multimodal mapping, we propose another 9 novel cross-modal and cross-pair retrieval tasks, called Omni-Retrieval pre-training. The pre-training is conducted in an efficient manner with only two forward/backward updates for the combined 14 tasks. Extensive experiments and analysis show the effectiveness of each task. When combining all pre-training tasks, our model achieves state-of-the-art performance on 7 commerce-related downstream tasks after fine-tuning. Additionally, we propose a novel approach of modality randomization to dynamically adjust our model under different efficiency constraints.

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