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


Abstract
Visual question answering (VQA) is a hallmark of vision and language reasoningand 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.
Anthology ID:
2022.findings-emnlp.67
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
951–967
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.67
DOI:
10.18653/v1/2022.findings-emnlp.67
Bibkey:
Cite (ACL):
Anthony Meng Huat Tiong, Junnan Li, Boyang Li, Silvio Savarese, and Steven C.H. Hoi. 2022. Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 951–967, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training (Tiong et al., Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-emnlp.67.pdf
Video:
 https://aclanthology.org/2022.findings-emnlp.67.mp4