  Existing methods for vision-and-language learning typically require designing
task-specific architectures and objectives for each task. For example, a
multi-label answer classifier for visual question answering, a region scorer
for referring expression comprehension, and a language decoder for image
captioning, etc. To alleviate these hassles, in this work, we propose a unified
framework that learns different tasks in a single architecture with the same
language modeling objective, i.e., multimodal conditional text generation,
where our models learn to generate labels in text based on the visual and
textual inputs. On 7 popular vision-and-language benchmarks, including visual
question answering, referring expression comprehension, visual commonsense
reasoning, most of which have been previously modeled as discriminative tasks,
our generative approach (with a single unified architecture) reaches comparable
performance to recent task-specific state-of-the-art vision-and-language
models. Moreover, our generative approach shows better generalization ability
on questions that have rare answers. Also, we show that our framework allows
multi-task learning in a single architecture with a single set of parameters,
achieving similar performance to separately optimized single-task models. Our
code is publicly available at: https://github.com/j-min/VL-T5
