Text-video retrieval, a prominent sub-field within the broader domain of
multimedia content management, has witnessed remarkable growth and innovation
over the past decade. However, existing methods assume the video scenes are
consistent and the description annotators are unbiased. These limitations fail
to align with fluid real-world scenarios, and descriptions can be influenced by
annotator biases, diverse writing styles, and varying textual perspectives. To
overcome the aforementioned problems, we introduce WAVER, a cross-domain
knowledge distillation mechanism designed to tackle the challenge of handling
writing-style agnostics. WAVER capitalizes on the open-vocabulary properties
inherent in pre-trained vision-language models and employs an implicit
knowledge distillation approach to transfer text-based knowledge from a teacher
model to a vision-based student. Empirical studies conducted across four
standard benchmark datasets, encompassing various settings, provide compelling
evidence that \WAVER can achieve state-of-the-art performance in text-video
retrieval tasks while handling writing-style variations.

WAVER 是一种用于处理文本 - 视频检索任务中的写作风格变化的交叉领域知识蒸馏机制，通过利用预训练的视觉语言模型中的开放词汇属性，达到从教师模型向视觉学生模型转移文本知识的目的，并在四个标准基准数据集上的实证研究中取得了最先进的表现。