Abstract
Generic Event Boundary Detection (GEBD) aims to identify moments in videos that humans perceive as event boundaries. This paper proposes a novel method for addressing this task, called Structured Context Learning, which introduces the Structured Partition of Sequence (SPoS) to provide a structured context for learning temporal information. Our approach is end-to-end trainable and flexible, not restricted to specific temporal models like GRU, LSTM, and Transformers. This flexibility enables our method to achieve a better speed-accuracy trade-off. Specifically, we apply SPoS to partition the input frame sequence and provide a structured context for the subsequent temporal model. Notably, SPoS's overall computational complexity is linear with respect to the video length. We next calculate group similarities to capture differences between frames, and a lightweight fully convolutional network is utilized to determine the event boundaries based on the grouped similarity maps. To remedy the ambiguities of boundary annotations, we adapt the Gaussian kernel to preprocess the ground-truth event boundaries. Our proposed method has been extensively evaluated on the challenging Kinetics-GEBD, TAPOS, and shot transition detection datasets, demonstrating its superiority over existing state-of-the-art methods.
Abstract (translated)
通用事件边界检测（GEBD）旨在识别视频中人类感知为事件边界的时刻。本文提出了一种新颖的方法来解决这一任务，称为结构化上下文学习，引入了序列的结构划分（SPoS），以提供一种结构化的上下文用于时间信息的学习。我们的方法是端到端可训练且灵活的，并不受限于特定的时间模型如GRU、LSTM和Transformer等。这种灵活性使得我们的方法能够实现更好的速度-精度权衡。 具体而言，我们使用SPoS对输入帧序列进行划分并为后续的时间模型提供结构化上下文。值得注意的是，SPoS的整体计算复杂度与视频长度呈线性关系。接下来，我们计算组相似度以捕捉帧之间的差异，并利用轻量级全卷积网络根据分组的相似度图来确定事件边界。 为了缓解边界注释的模糊性问题，我们将高斯核应用于预处理地面实况事件边界。我们的方法已在具有挑战性的Kinetics-GEBD、TAPOS和镜头转换检测数据集上进行了广泛的评估，并证明了其优于现有最先进的方法的优势。
URL
https://arxiv.org/abs/2512.00475
