A Good Prompt Is Worth Millions of Parameters? Low-resource Prompt-based Learning for Vision-Language Models

• URL: http://arxiv.org/abs/2110.08484v1
• Date: Sat, 16 Oct 2021 06:07:59 GMT
• Title: A Good Prompt Is Worth Millions of Parameters? Low-resource Prompt-based Learning for Vision-Language Models
• Authors: Woojeong Jin, Yu Cheng, Yelong Shen, Weizhu Chen, Xiang Ren
• Abstract summary: FewVLM is a few-shot prompt-based learner on vision-language tasks. We pretrain a sequence-to-sequence Transformer model with both prefix language modeling (PrefixLM) and masked language modeling (MaskedLM) We observe that prompts significantly affect zero-shot performance but marginally affect few-shot performance.
• Score: 50.27305012063483
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large pretrained vision-language (VL) models can learn a new task with a handful of examples or generalize to a new task without fine-tuning. However, these gigantic VL models are hard to deploy for real-world applications due to their impractically huge model size and slow inference speed. In this work, we propose FewVLM, a few-shot prompt-based learner on vision-language tasks. We pretrain a sequence-to-sequence Transformer model with both prefix language modeling (PrefixLM) and masked language modeling (MaskedLM), and introduce simple prompts to improve zero-shot and few-shot performance on VQA and image captioning. Experimental results on five VQA and captioning datasets show that \method\xspace outperforms Frozen which is 31 times larger than ours by 18.2% point on zero-shot VQAv2 and achieves comparable results to a 246$\times$ larger model, PICa. We observe that (1) prompts significantly affect zero-shot performance but marginally affect few-shot performance, (2) MaskedLM helps few-shot VQA tasks while PrefixLM boosts captioning performance, and (3) performance significantly increases when training set size is small.

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