Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking

Abstract

Contrastive learning has gained widespread adoption for retrieval tasks due to its minimal requirement for manual annotations. However, popular training frameworks typically learn from binary (positive/negative) relevance, making them ineffective at incorporating desired rankings. As a result, the poor ranking performance of these models forces systems to employ a re-ranker, which increases complexity, maintenance effort and inference time. To address this, we introduce Generalized Contrastive Learning (GCL), a training framework designed to learn from continuous ranking scores beyond binary relevance. GCL encodes both relevance and ranking information into a unified embedding space by applying ranking scores to the loss function. This enables a single-stage retrieval system. In addition, during our research, we identified a lack of public multi-modal datasets that benchmark both retrieval and ranking capabilities. To facilitate this and future research for ranked retrieval, we curated a large-scale MarqoGS-10M dataset using GPT-4 and Google Shopping, providing ranking scores for each of the 10 million query-document pairs. Our results show that GCL achieves a 29.3% increase in NDCG@10 for in-domain evaluations and 6.0% to 10.0% increases for cold-start evaluations compared to the finetuned CLIP baseline with MarqoGS-10M. Additionally, we evaluated GCL offline on a proprietary user interaction data. GCL shows an 11.2% gain for in-domain evaluations. The dataset and the method are available at: https://github.com/marqo-ai/GCL.