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[pdf]
[arXiv]
[bibtex]@InProceedings{Srinivasan_2023_CVPR, author = {Srinivasan, Tejas and Ren, Xiang and Thomason, Jesse}, title = {Curriculum Learning for Data-Efficient Vision-Language Alignment}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {5619-5624} }
Curriculum Learning for Data-Efficient Vision-Language Alignment
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 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 vision-language 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-OD models to each other using TOnICS outperforms CLIP on downstream zero-shot image retrieval using < 1% as much training data.
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