Abstract
What does it mean for two videos to be similar? Videos may appear similar when judged by the actions they depict, yet entirely different if evaluated based on the locations where they were filmed. While humans naturally compare videos by taking different aspects into account, this ability has not been thoroughly studied and presents a challenge for models that often depend on broad global similarity scores. Large Multimodal Models (LMMs) with video understanding capabilities open new opportunities for leveraging natural language in comparative video tasks. We introduce Concept-based Video Similarity estimation (ConViS), a novel task that compares pairs of videos by computing interpretable similarity scores across a predefined set of key semantic concepts. ConViS allows for human-like reasoning about video similarity and enables new applications such as concept-conditioned video retrieval. To support this task, we also introduce ConViS-Bench, a new benchmark comprising carefully annotated video pairs spanning multiple domains. Each pair comes with concept-level similarity scores and textual descriptions of both differences and similarities. Additionally, we benchmark several state-of-the-art models on ConViS, providing insights into their alignment with human judgments. Our results reveal significant performance differences on ConViS, indicating that some concepts present greater challenges for estimating video similarity. We believe that ConViS-Bench will serve as a valuable resource for advancing research in language-driven video understanding.
Abstract (translated)
两个视频相似意味着什么？根据它们所描绘的动作来判断，两个视频可能会显得很相似；但如果依据拍摄地点来进行评估，则可能完全不同。人类在比较视频时会自然地考虑不同的方面，但这一能力尚未被充分研究，并且对于依赖广泛全局相似度分数的模型来说是一个挑战。具备视频理解功能的大规模多模态模型（LMM）为在对比视频任务中利用自然语言带来了新的机会。 我们引入了基于概念的视频相似性估计（ConViS），这是一个新颖的任务，通过计算一组预定义的关键语义概念跨视频对的可解释相似度分数来比较视频。ConViS 允许像人类一样推理关于视频的相似性，并且能够支持如基于概念条件的视频检索等新的应用。 为了支持这一任务，我们还引入了 ConViS-Bench，一个新的基准测试库，它包含精心标注的跨多个领域的视频对。每一对视频都带有概念级别的相似度分数和差异及相似性的文本描述。 此外，我们在 ConViS 上评估了几种最先进的模型，提供了关于它们与人类判断一致程度的见解。我们的结果表明，在 ConViS 任务上存在显著的表现差距，这表明某些概念对于估计视频相似性来说更具挑战性。 我们认为，ConViS-Bench 将成为推动基于语言的视频理解研究的重要资源。
URL
https://arxiv.org/abs/2509.19245
