TxT: Crossmodal End-to-End Learning with Transformers

• URL: http://arxiv.org/abs/2109.04422v1
• Date: Thu, 9 Sep 2021 17:12:20 GMT
• Title: TxT: Crossmodal End-to-End Learning with Transformers
• Authors: Jan-Martin O. Steitz, Jonas Pfeiffer, Iryna Gurevych, Stefan Roth
• Abstract summary: Reasoning over multiple modalities requires an alignment of semantic concepts across domains. TxT is a transformer-based crossmodal pipeline that enables fine-tuning both language and visual components on the downstream task. Our model achieves considerable gains from end-to-end learning for multimodal question answering.
• Score: 84.55645255507461
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reasoning over multiple modalities, e.g. in Visual Question Answering (VQA), requires an alignment of semantic concepts across domains. Despite the widespread success of end-to-end learning, today's multimodal pipelines by and large leverage pre-extracted, fixed features from object detectors, typically Faster R-CNN, as representations of the visual world. The obvious downside is that the visual representation is not specifically tuned to the multimodal task at hand. At the same time, while transformer-based object detectors have gained popularity, they have not been employed in today's multimodal pipelines. We address both shortcomings with TxT, a transformer-based crossmodal pipeline that enables fine-tuning both language and visual components on the downstream task in a fully end-to-end manner. We overcome existing limitations of transformer-based detectors for multimodal reasoning regarding the integration of global context and their scalability. Our transformer-based multimodal model achieves considerable gains from end-to-end learning for multimodal question answering.

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