2025/05/05 by Simon Ging, Sebastian Walter, Ging, Simon +9 · 1 voice
Computer Science · #Advanced Graph Neural Networks #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Natural Language Processing Techniques #Topic Modeling #cs.CL #cs.CV #cs.IR #cs.LG
paper · pdf · doi:10.48550/arxiv.2505.02746
openalex publication_date 2025/05/05 · arxiv published 2025/05/05 · arxiv updated 2025/09/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Training high-quality CLIP models typically requires enormous datasets, which limits the development of domain-specific models -- especially in areas that even the largest CLIP models do not cover well -- and drives up training costs. This poses challenges for scientific research that needs fine-grained control over the training procedure of CLIP models. In this work, we show that by employing smart web search strategies enhanced with knowledge graphs, a robust CLIP model can be trained from scratch with considerably less data. Specifically, we demonstrate that an expert foundation model for living organisms can be built using just 10M images. Moreover, we introduce EntityNet, a dataset comprising 33M images paired with 46M text descriptions, which enables the training of a generic CLIP model in significantly reduced time.