Abstract
Video summarization is a crucial research area that aims to efficiently browse and retrieve relevant information from the vast amount of video content available today. With the exponential growth of multimedia data, the ability to extract meaningful representations from videos has become essential. Video summarization techniques automatically generate concise summaries by selecting keyframes, shots, or segments that capture the video's essence. This process improves the efficiency and accuracy of various applications, including video surveillance, education, entertainment, and social media. Despite the importance of video summarization, there is a lack of diverse and representative datasets, hindering comprehensive evaluation and benchmarking of algorithms. Existing evaluation metrics also fail to fully capture the complexities of video summarization, limiting accurate algorithm assessment and hindering the field's progress. To overcome data scarcity challenges and improve evaluation, we propose an unsupervised approach that leverages video data structure and information for generating informative summaries. By moving away from fixed annotations, our framework can produce representative summaries effectively. Moreover, we introduce an innovative evaluation pipeline tailored specifically for video summarization. Human participants are involved in the evaluation, comparing our generated summaries to ground truth summaries and assessing their informativeness. This human-centric approach provides valuable insights into the effectiveness of our proposed techniques. Experimental results demonstrate that our training-free framework outperforms existing unsupervised approaches and achieves competitive results compared to state-of-the-art supervised methods.
Abstract (translated)
视频摘要是一个关键的研究领域，旨在有效地浏览和检索当今大量视频内容。随着多媒体数据的指数增长，从视频中提取有意义的表示已成为必不可少的。视频摘要技术通过选择关键帧、镜头或片段来捕捉视频的本质，自动生成简洁的摘要。这个过程提高了各种应用（包括视频监控、教育、娱乐和社交媒体）的效率和准确性。尽管视频摘要非常重要，但缺乏多样且具有代表性的数据集，限制了全面评估和基准测试算法的准确性。现有的评估指标也没有完全捕捉到视频摘要的复杂性，从而限制了对准确算法评估和领域进步的限制。为了克服数据稀缺的挑战，提高评估，我们提出了一个无监督的方法，利用视频数据结构和信息生成有信息的摘要。通过远离固定注释，我们的框架可以有效地生成代表性的摘要。此外，我们还引入了一个专门针对视频摘要的创新评估管道。人类参与者参与了评估，将生成的摘要与真实摘要进行比较，并评估其信息价值。这种以人为中心的方法提供了对我们所提技术的有效性的宝贵见解。实验结果表明，我们的无监督框架优于现有的无监督方法，并且在与最先进的监督方法相比之下取得了竞争力的结果。
URL
https://arxiv.org/abs/2404.04564
