RankCLIP: Ranking-Consistent Language-Image Pretraining
<|reference_start|>RankCLIP: Ranking-Consistent Language-Image Pretraining: 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 pretraining 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.<|reference_end|>
@article{zhang2024rankclip:,
    title={RankCLIP: Ranking-Consistent Language-Image Pretraining},
    author={Yiming Zhang, Zhuokai Zhao, Zhaorun Chen, Zhili Feng, Zenghui Ding,
  Yining Sun},
    journal={arXiv preprint arXiv:2404.09387},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.09387},
    primaryClass={cs.CV cs.AI cs.LG}
}