Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training

17 Oct 2022  ·  Anthony Meng Huat Tiong, Junnan Li, Boyang Li, Silvio Savarese, Steven C. H. Hoi ·

Visual question answering (VQA) is a hallmark of vision and language reasoning and a challenging task under the zero-shot setting. We propose Plug-and-Play VQA (PNP-VQA), a modular framework for zero-shot VQA. In contrast to most existing works, which require substantial adaptation of pretrained language models (PLMs) for the vision modality, PNP-VQA requires no additional training of the PLMs. Instead, we propose to use natural language and network interpretation as an intermediate representation that glues pretrained models together. We first generate question-guided informative image captions, and pass the captions to a PLM as context for question answering. Surpassing end-to-end trained baselines, PNP-VQA achieves state-of-the-art results on zero-shot VQAv2 and GQA. With 11B parameters, it outperforms the 80B-parameter Flamingo model by 8.5% on VQAv2. With 738M PLM parameters, PNP-VQA achieves an improvement of 9.1% on GQA over FewVLM with 740M PLM parameters. Code is released at https://github.com/salesforce/LAVIS/tree/main/projects/pnp-vqa

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Visual Question Answering GQA test-dev PNP-VQA Accuracy 41.9 # 8
Visual Question Answering OK-VQA PNP-VQA Accuracy 35.9 # 15
Visual Question Answering VQA v2 test-dev PNP-VQA Accuracy 64.8 # 40
Visual Question Answering VQA v2 val PNP-VQA Accuracy 63.3 # 5

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