KAT: A Knowledge Augmented Transformer for Vision-and-Language (2022.naacl-main)
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| Challenge: | Existing methods for knowledge retrieval and answer prediction have left open questions about the quality and relevance of the retrieved knowledge and how the reasoning processes over implicit and explicit knowledge should be integrated. |
| Approach: | They propose a Knowledge Augmented Transformer which integrates both implicit and explicit knowledge in an encoder-decoder architecture while simultaneously reasoning over both knowledge sources during answer generation. |
| Outcome: | The proposed model achieves a strong state-of-the-art (+6% absolute) on the open-domain multimodal task of OK-VQA. |
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