Papers › Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training
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
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Visual Question Answering (VQA) | GQA test-dev | PNP-VQA | Accuracy | 41.9 | #13 of 17 | Archive leaderboard | report |
| Visual Question Answering (VQA) | OK-VQA | PNP-VQA | Accuracy | 35.9 | #31 of 37 | Archive leaderboard | report |
| Visual Question Answering (VQA) | VQA v2 test-dev | PNP-VQA | Accuracy | 64.8 | #44 of 56 | Archive leaderboard | report |
| Visual Question Answering (VQA) | VQA v2 val | PNP-VQA | Accuracy | 63.3 | #2 of 11 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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