Visual Captioning at Will: Describing Images and Videos Guided by a Few
  Stylized Sentences
Dingyi Yang Hongyu Chen Xinglin Hou Tiezheng Ge Yuning Jiang Qin Jin
Stylized visual captioning aims to generate image or video descriptions with
specific styles, making them more attractive and emotionally appropriate. One
major challenge with this task is the lack of paired stylized captions for
visual content, so most existing works focus on unsupervised methods that do
not rely on parallel datasets. However, these approaches still require training
with sufficient examples that have style labels, and the generated captions are
limited to predefined styles. To address these limitations, we explore the
problem of Few-Shot Stylized Visual Captioning, which aims to generate captions
in any desired style, using only a few examples as guidance during inference,
without requiring further training. We propose a framework called FS-StyleCap
for this task, which utilizes a conditional encoder-decoder language model and
a visual projection module. Our two-step training scheme proceeds as follows:
first, we train a style extractor to generate style representations on an
unlabeled text-only corpus. Then, we freeze the extractor and enable our
decoder to generate stylized descriptions based on the extracted style vector
and projected visual content vectors. During inference, our model can generate
desired stylized captions by deriving the style representation from
user-supplied examples. Our automatic evaluation results for few-shot
sentimental visual captioning outperform state-of-the-art approaches and are
comparable to models that are fully trained on labeled style corpora. Human
evaluations further confirm our model s ability to handle multiple styles.