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RankCLIP: Ranking-Consistent Language-Image Pretraining
April 16, 2024, 4:43 a.m. | Yiming Zhang, Zhuokai Zhao, Zhaorun Chen, Zhili Feng, Zenghui Ding, Yining Sun
cs.LG updates on arXiv.org arxiv.org
Abstract: Among the ever-evolving development of vision-language models, contrastive language-image pretraining (CLIP) has set new benchmarks in many downstream tasks such as zero-shot classifications by leveraging self-supervised contrastive learning on large amounts of text-image pairs. However, its 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 …
abstract arxiv benchmarks clip consistent cs.ai cs.cv cs.lg development ever however image language language models pretraining ranking relationships set tasks text text-image type vision vision-language models zero-shot
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