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arXiv · 2404.09387

RankCLIP: Ranking-Consistent Language-Image Pretraining

Yiming Zhang·Zhuokai Zhao·Zhaorun Chen·Zhili Feng·Zenghui Ding·Yining Sun
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Abstract

Self-supervised contrastive learning models, such as CLIP, have set new benchmarks for vision-language models in many downstream tasks. However, their dependency on rigid one-to-one mappings overlooks the complex and often multifaceted relationships between and within texts and images. To this end, we introduce RankCLIP, a novel pre-training method that extends beyond the rigid one-to-one matching framework of CLIP and its variants. By extending the traditional pair-wise loss to list-wise, and leveraging both in-modal and cross-modal ranking consistency, RankCLIP improves the alignment process, enabling it to capture the nuanced many-to-many relationships between and within each modality. Through comprehensive experiments, we demonstrate the effectiveness of RankCLIP in various downstream tasks, notably achieving significant gains in zero-shot classifications over state-of-the-art methods, underscoring the importance of this enhanced learning process.

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Yiming Zhang, Zhuokai Zhao, Zhaorun Chen, Zhili Feng, Zenghui Ding, Yining Sun. 2024-04-15. RankCLIP: Ranking-Consistent Language-Image Pretraining. https://arxiv.org/abs/2404.09387

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