Curriculum Learning for Data-Efficient Vision-Language Alignment

• URL: http://arxiv.org/abs/2207.14525v1
• Date: Fri, 29 Jul 2022 07:45:56 GMT
• Title: Curriculum Learning for Data-Efficient Vision-Language Alignment
• Authors: Tejas Srinivasan, Xiang Ren, Jesse Thomason
• Abstract summary: 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. TOnICS outperforms CLIP on downstream zero-shot image retrieval while using less than 1% as much training data.
• Score: 29.95935291982015
• License: http://creativecommons.org/licenses/by/4.0/
• 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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