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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.findings-emnlp.67.pdf