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Pre-training image-language transformers for open-vocabulary tasks

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arxiv 2209.04372 v1 pith:23XRBAGG submitted 2022-09-09 cs.CV

AJ Piergiovanni , Weicheng Kuo , Anelia Angelova This is my paper · ORCID
classification cs.CV
keywords pre-trainingtaskscaptioninglanguagevisionvisualadditionalanswering
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We present a pre-training approach for vision and language transformer models, which is based on a mixture of diverse tasks. We explore both the use of image-text captioning data in pre-training, which does not need additional supervision, as well as object-aware strategies to pre-train the model. We evaluate the method on a number of textgenerative vision+language tasks, such as Visual Question Answering, visual entailment and captioning, and demonstrate large gains over standard pre-training methods.

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Cited by 1 Pith paper

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  1. Adapting Lightweight Vision Language Models for Radiological Visual Question Answering

    cs.CV 2025-06 reject novelty 4.0 of 10

    A 3B PaliGemma model fine-tuned with synthetic QA pairs and two-stage training reaches 41.5% accuracy on open-ended radiology VQA, about 15 points below LLaVA-Med.

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