Curriculum Learning for Data-Efficient Vision-Language Alignment

Tejas Srinivasan; Xiang Ren; Jesse Thomason

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arXiv · 2207.14525

Curriculum Learning for Data-Efficient Vision-Language Alignment

Tejas Srinivasan·Xiang Ren·Jesse Thomason

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Abstract

Aligning image and text encoders from scratch using contrastive learning requires large amounts of paired image-text data. We alleviate this need by aligning individually pre-trained language and vision representation models using a much smaller amount of paired data, augmented with a curriculum learning algorithm to learn fine-grained vision-language alignments. TOnICS (Training with Ontology-Informed Contrastive Sampling) initially samples minibatches whose image-text pairs contain a wide variety of objects to learn object-level alignment, and progressively samples minibatches where all image-text pairs contain the same object to learn finer-grained contextual alignment. Aligning pre-trained BERT and VinVL models to each other using TOnICS outperforms CLIP on downstream zero-shot image retrieval while using less than 1% as much training data.

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BibTeX RIS

Tejas Srinivasan, Xiang Ren, Jesse Thomason. 2022-07-29. Curriculum Learning for Data-Efficient Vision-Language Alignment. https://arxiv.org/abs/2207.14525

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