Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge

• URL: http://arxiv.org/abs/2101.06013v1
• Date: Fri, 15 Jan 2021 08:37:55 GMT
• Title: Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge
• Authors: Violetta Shevchenko, Damien Teney, Anthony Dick, Anton van den Hengel
• Abstract summary: This paper investigates the injection of knowledge from general-purpose knowledge bases (KBs) into vision-and-language transformers. We empirically study the relevance of various KBs to multiple tasks and benchmarks. The technique is model-agnostic and can expand the applicability of any vision-and-language transformer with minimal computational overhead.
• Score: 59.87823082513752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The limits of applicability of vision-and-language models are defined by the coverage of their training data. Tasks like vision question answering (VQA) often require commonsense and factual information beyond what can be learned from task-specific datasets. This paper investigates the injection of knowledge from general-purpose knowledge bases (KBs) into vision-and-language transformers. We use an auxiliary training objective that encourages the learned representations to align with graph embeddings of matching entities in a KB. We empirically study the relevance of various KBs to multiple tasks and benchmarks. The technique brings clear benefits to knowledge-demanding question answering tasks (OK-VQA, FVQA) by capturing semantic and relational knowledge absent from existing models. More surprisingly, the technique also benefits visual reasoning tasks (NLVR2, SNLI-VE). We perform probing experiments and show that the injection of additional knowledge regularizes the space of embeddings, which improves the representation of lexical and semantic similarities. The technique is model-agnostic and can expand the applicability of any vision-and-language transformer with minimal computational overhead.

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