Abstract
With the rapid development of AI-generated content (AIGC), the creation of high-quality AI-generated videos has become faster and easier, resulting in the Internet being flooded with all kinds of video content. However, the impact of these videos on the content ecosystem remains largely unexplored. Video information retrieval remains a fundamental approach for accessing video content. Building on the observation that retrieval models often favor AI-generated content in ad-hoc and image retrieval tasks, we investigate whether similar biases emerge in the context of challenging video retrieval, where temporal and visual factors may further influence model behavior. To explore this, we first construct a comprehensive benchmark dataset containing both real and AI-generated videos, along with a set of fair and rigorous metrics to assess bias. This benchmark consists of 13,000 videos generated by two state-of-the-art open-source video generation models. We meticulously design a suite of rigorous metrics to accurately measure this preference, accounting for potential biases arising from the limited frame rate and suboptimal quality of AIGC videos. We then applied three off-the-shelf video retrieval models to perform retrieval tasks on this hybrid dataset. Our findings reveal a clear preference for AI-generated videos in retrieval. Further investigation shows that incorporating AI-generated videos into the training set of retrieval models exacerbates this bias. Unlike the preference observed in image modalities, we find that video retrieval bias arises from both unseen visual and temporal information, making the root causes of video bias a complex interplay of these two factors. To mitigate this bias, we fine-tune the retrieval models using a contrastive learning approach. The results of this study highlight the potential implications of AI-generated videos on retrieval systems.
Abstract (translated)
随着人工智能生成内容（AIGC）的快速发展，高质量的人工智能视频创作变得更快且更容易。这导致互联网上充斥着各种各样的视频内容。然而，这些视频对内容生态系统的影响仍然很大程度上未被探索。视频信息检索仍然是访问视频内容的基本方法之一。鉴于在即席查询和图像检索任务中，检索模型往往更倾向于人工智能生成的内容，我们探讨了这种偏见是否也会出现在具有挑战性的视频检索情境下，在其中时间因素与视觉因素可能会进一步影响模型的行为表现。 为了解决这一问题，我们首先构建了一个全面的基准数据集，其中包括真实的和由AI生成的视频，并且制定了一套公平严谨的标准来评估这些偏见。该基准包括了13,000个由两种最先进的开源视频生成模型创建的视频样本。我们精心设计了一系列严格的指标以准确测量这种偏好，考虑到潜在源于AIGC视频帧率有限和质量不佳所导致的偏差。 接下来，我们将三种现成的视频检索模型应用于这个混合数据集上进行检索任务。我们的研究发现表明，在检索过程中存在着对AI生成视频明显的偏爱倾向。进一步的研究显示，将AI生成的视频纳入到检索模型训练集中会加剧这种偏见问题。与图像模态中观察到的偏好不同，我们发现视频检索中的偏差来源于未见过的视觉信息和时间信息，因此视频偏见的根本原因在于这两种因素之间的复杂相互作用。 为缓解这一偏见，我们将使用对比学习方法对这些检索模型进行微调处理。这项研究的结果突显了AI生成视频可能给检索系统带来的潜在影响。
URL
https://arxiv.org/abs/2502.07327
