As single-task accuracy on individual language and image tasks has improved substantially in the last few years, the long-term goal of a generally skilled agent that can both see and talk becomes more feasible to explore. In this work, we focus on leveraging existing individual language and image tasks, along with resources that incorporate both vision and language towards that objective. We explore architectures that combine state-of-the-art Transformer and ResNeXt modules fed into a  multimodal module to produce a combined model trained on many tasks. We provide a thorough analysis of the components of the model, and transfer performance when training on one, some, or all of the tasks. Our final models provide a single system that obtains good results on all vision and language tasks considered, and improves the state of the art in image-grounded conversational applications.
As single-task accuracy on individual language and image tasks has improved substantially in the last few years, the long-term goal of a generally skilled agent that can both see and talk becomes more feasible to explore. In this work, we focus on leveraging individual language and image tasks, along with resources that incorporate both vision and language towards that objective. We design an architecture that combines state-of-the-art Transformer and ResNeXt modules fed into a novel attentive multimodal module to produce a combined model trained on many tasks. We provide a thorough analysis of the components of the model, and transfer performance when training on one, some, or all of the tasks. Our final models provide a single system that obtains good results on all vision and language tasks considered, and improves the state-of-the-art in image-grounded conversational applications.
D
existing
R
explore architectures that combine
design an architecture that combines
A
novel attentive
R
state of the art
state-of-the-art