Abstract
Image-Language Foundation Models (ILFM) have demonstrated remarkable success in image-text understanding/generation tasks, providing transferable multimodal representations that generalize across diverse downstream image-based tasks. The advancement of video-text research has spurred growing interest in extending image-based models to the video domain. This paradigm, known as image-to-video transfer learning, succeeds in alleviating the substantial data and computational requirements associated with training video-language foundation models from scratch for video-text learning. This survey provides the first comprehensive review of this emerging field, which begins by summarizing the widely used ILFM and their capabilities. We then systematically classify existing image-to-video transfer learning strategies into two categories: frozen features and modified features, depending on whether the original representations from ILFM are preserved or undergo modifications. Building upon the task-specific nature of image-to-video transfer, this survey methodically elaborates these strategies and details their applications across a spectrum of video-text learning tasks, ranging from fine-grained (e.g., spatio-temporal video grounding) to coarse-grained (e.g., video question answering). We further present a detailed experimental analysis to investigate the efficacy of different image-to-video transfer learning paradigms on a range of downstream video understanding tasks. Finally, we identify prevailing challenges and highlight promising directions for future research. By offering a comprehensive and structured overview, this survey aims to establish a structured roadmap for advancing video-text learning based on existing ILFM, and to inspire future research directions in this rapidly evolving domain.
Abstract (translated)
图像-语言基础模型（ILFM）在图像文本理解和生成任务中展示了显著的成功，提供了可转移的多模态表示形式，这些表示能够在各种下游基于图像的任务中进行泛化。视频文本研究的进步激发了将基于图像的模型扩展到视频领域的日益增长的兴趣。这一范式被称为从图像到视频的迁移学习，在缓解训练用于视频文本学习的视频语言基础模型所需的大量数据和计算资源需求方面取得了成功。 这篇综述提供了对该新兴领域首次全面的回顾，首先总结了广泛使用的ILFM及其能力。然后，我们系统地将现有的图像到视频的迁移学习策略分为两类：冻结特征和修改特征，这取决于原始ILFM表示是否被保留或进行修改。基于从图像到视频迁移的任务特定性质，该综述详细阐述这些策略，并介绍了它们在一系列视频文本学习任务中的应用，从细粒度（例如时空视频定位）到粗粒度（例如视频问答）。我们进一步进行了详细的实验分析，以研究不同图像到视频的迁移学习范式对各种下游视频理解任务的有效性。最后，我们识别了当前存在的挑战，并指出了未来研究中具有前景的方向。 通过提供全面和结构化的概述，本综述旨在为基于现有ILFM的视频文本学习建立一个有条理的发展路线图，并激发这一迅速发展的领域中的未来研究方向。
URL
https://arxiv.org/abs/2510.10671
