1.7k citations · 4.4k across the 16 of their papers we have counts for
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X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers
Jaemin Cho, Jiasen Lu, Dustin Schwenk +2
Mirroring the success of masked language models, vision-and-language counterparts like ViLBERT, LXMERT and UNITER have achieved state of the art performance on a variety of multimo…
Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data
Michael Cogswell, Jiasen Lu, Rishabh Jain +3
Can we develop visually grounded dialog agents that can efficiently adapt to new tasks without forgetting how to talk to people? Such agents could leverage a larger variety of exis…
Spatially Aware Multimodal Transformers for TextVQA
Yash Kant, Dhruv Batra, Peter Anderson +4
Textual cues are essential for everyday tasks like buying groceries and using public transport. To develop this assistive technology, we study the TextVQA task, i.e., reasoning abo…