2024/02/23 by Hyunjae Kim, Kim, Hyunjae, Seunghyun Yoon +11 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Algorithms and Data Compression #Artificial Intelligence (cs.AI) #Coding theory and cryptography #Computer Vision and Pattern Recognition (cs.CV) #DNA and Biological Computing #FOS: Computer and information sciences #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2402.15120
openalex publication_date 2024/02/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Contrastive language-image pre-training (CLIP) models have demonstrated considerable success across various vision-language tasks, such as text-to-image retrieval, where the model is required to effectively process natural language input to produce an accurate visual output. However, current models still face limitations in dealing with linguistic variations in input queries, such as paraphrases, making it challenging to handle a broad range of user queries in real-world applications. In this study, we introduce a straightforward fine-tuning approach to enhance the representations of CLIP models for paraphrases. Our approach involves a two-step paraphrase generation process, where we automatically create two categories of paraphrases from web-scale image captions by leveraging large language models. Subsequently, we fine-tune the CLIP text encoder using these generated paraphrases while freezing the image encoder. Our resulting model, which we call ParaCLIP, exhibits significant improvements over baseline CLIP models across various tasks, including paraphrased retrieval (with rank similarity scores improved by up to 2.0% and 5.6%), Visual Genome Relation and Attribution, as well as seven semantic textual similarity tasks.