  In recent years, the pre-training-then-fine-tuning paradigm has yielded
immense success on a wide spectrum of cross-modal tasks, such as visual
question answering (VQA), in which a visual-language (VL) model is first
optimized via self-supervised task objectives, e.g., masked language modeling
(MLM) and image-text matching (ITM), and then fine-tuned to adapt to downstream
task (e.g., VQA) via a brand-new objective function, e.g., answer prediction.
The inconsistency of the objective forms not only severely limits the
generalization of pre-trained VL models to downstream tasks, but also requires
a large amount of labeled data for fine-tuning. To alleviate the problem, we
propose an innovative VL fine-tuning paradigm (named Declaration-based Prompt
Tuning, abbreviated as DPT), which jointly optimizes the objectives of
pre-training and fine-tuning of VQA model, boosting the effective adaptation of
pre-trained VL models to the downstream task. Specifically, DPT reformulates
the objective form of VQA task via (1) textual adaptation, which converts the
given questions into declarative sentence-form for prompt-tuning, and (2) task
adaptation, which optimizes the objective function of VQA problem in the manner
of pre-training phase. Experimental results on GQA dataset show that DPT
outperforms the fine-tuned counterpart by a large margin regarding accuracy in
both fully-supervised (2.68%) and zero-shot/few-shot (over 31%) settings. All
the data and codes will be available to facilitate future research.
