e-CLIP: Large-Scale Vision-Language Representation Learning in E-commerce
This work addresses domain-specific challenges in e-commerce for search and recommendation applications, but it is incremental as it builds on existing contrastive learning methods.
The paper tackles the problem of aligning vision and language representations for e-commerce product content using a contrastive learning framework, resulting in improved performance across multiple downstream tasks such as category classification and product matching compared to baselines.
Understanding vision and language representations of product content is vital for search and recommendation applications in e-commerce. As a backbone for online shopping platforms and inspired by the recent success in representation learning research, we propose a contrastive learning framework that aligns language and visual models using unlabeled raw product text and images. We present techniques we used to train large-scale representation learning models and share solutions that address domain-specific challenges. We study the performance using our pre-trained model as backbones for diverse downstream tasks, including category classification, attribute extraction, product matching, product clustering, and adult product recognition. Experimental results show that our proposed method outperforms the baseline in each downstream task regarding both single modality and multiple modalities.