Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training
Anthony Meng Huat Tiong, Junnan Li, Boyang Li, Silvio Savarese, Steven C. H. Hoi · 2022
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 (Goyal et al., 2017) and GQA (Hudson and Manning, 2019).With 11B parameters, it outperforms the 80B-parameter Flamingo model (Alayrac et al., 2022) by 8.5% on VQAv2.With 738M PLM parameters, PNP-VQA achieves an improvement of 9.1% on GQA over FewVLM (Jin et al., 2022) with 740M PLM parameters.