While recent progress in video-text retrieval has been driven by the
exploration of powerful model architectures and training strategies, the
representation learning ability of video-text retrieval models is still limited
due to low-quality and scarce training data annotations. To address this issue,
we present a novel video-text learning paradigm, HaVTR, which augments video
and text data to learn more generalized features. Specifically, we first adopt
a simple augmentation method, which generates self-similar data by randomly
duplicating or dropping subwords and frames. In addition, inspired by the
recent advancement in visual and language generative models, we propose a more
powerful augmentation method through textual paraphrasing and video stylization
using large language models (LLMs) and visual generative models (VGMs).
Further, to bring richer information into video and text, we propose a
hallucination-based augmentation method, where we use LLMs and VGMs to generate
and add new relevant information to the original data. Benefiting from the
enriched data, extensive experiments on several video-text retrieval benchmarks
demonstrate the superiority of HaVTR over existing methods.

HaVTR 提出了一种新颖的视频 - 文本学习范式，通过数据增强和引入生成模型生成更加丰富的数据，提高视频 - 文本检索模型的表示学习能力，在多个检索基准测试中展现了优越性。

HaVTR：利用大型基础模型改进视频 - 文本检索的方法

HaVTR: Improving Video-Text Retrieval Through Augmentation Using Large  Foundation Models

Recent progress in visual generative models enables the generation of
high-quality images. To prevent the misuse of generated images, it is important
to identify the origin model that generates them. In this work, we study the
origin attribution of generated images in a practical setting where only a few
images generated by a source model are available and the source model cannot be
accessed. The goal is to check if a given image is generated by the source
model. We first formulate this problem as a few-shot one-class classification
task. To solve the task, we propose OCC-CLIP, a CLIP-based framework for
few-shot one-class classification, enabling the identification of an image's
source model, even among multiple candidates. Extensive experiments
corresponding to various generative models verify the effectiveness of our
OCC-CLIP framework. Furthermore, an experiment based on the recently released
DALL-E 3 API verifies the real-world applicability of our solution.

在本文中，我们研究了在只有少量由源模型生成的图像可用且无法访问源模型的实际环境中，识别生成图像的来源模型的问题。我们提出了一种基于 CLIP 的少样本单类别分类框架 OCC-CLIP，可以在多个候选模型中辨别一个图像的来源模型。通过与各种生成模型对应的大量实验证实了我们的 OCC-CLIP 框架的有效性。此外，基于最近发布的 DALL-E 3 API 的实验验证了我们解决方案的实际应用性。