Abstract
Generic event boundary detection (GEBD) aims to identify natural boundaries in a video, segmenting it into distinct and meaningful chunks. Despite the inherent subjectivity of event boundaries, previous methods have focused on deterministic predictions, overlooking the diversity of plausible solutions. In this paper, we introduce a novel diffusion-based boundary detection model, dubbed DiffGEBD, that tackles the problem of GEBD from a generative perspective. The proposed model encodes relevant changes across adjacent frames via temporal self-similarity and then iteratively decodes random noise into plausible event boundaries being conditioned on the encoded features. Classifier-free guidance allows the degree of diversity to be controlled in denoising diffusion. In addition, we introduce a new evaluation metric to assess the quality of predictions considering both diversity and fidelity. Experiments show that our method achieves strong performance on two standard benchmarks, Kinetics-GEBD and TAPOS, generating diverse and plausible event boundaries.
Abstract (translated)
泛化事件边界检测（GEBD）的目标是识别视频中的自然边界，将其分割成不同的、有意义的片段。尽管事件边界的主观性决定了其多样性，但先前的方法主要集中在确定性的预测上，忽略了可能解决方案的多样性。在本文中，我们引入了一种基于扩散模型的新方法——DiffGEBD，从生成式的角度来解决GEBD问题。所提出的方法通过时间自相似性编码相邻帧之间的相关变化，并在此基础上迭代地将随机噪声解码为受特征条件约束的可能事件边界。无分类器引导允许在去噪扩散过程中控制多样性的程度。此外，我们还引入了一种新的评估指标来衡量预测的质量，同时考虑多样性和保真度。实验表明，我们的方法在Kinetics-GEBD和TAPOS两个标准基准测试上取得了优异的成绩，能够生成多样化且合理的事件边界。
URL
https://arxiv.org/abs/2508.12084
