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.

视频摘要是一个至关重要的研究领域，旨在从当今海量视频内容中高效浏览和检索相关信息。尽管视频摘要的重要性，缺乏多样化和有代表性的数据集阻碍了算法的全面评估和基准测试。为了克服数据稀缺的挑战并改进评估，我们提出了一种利用视频数据结构和信息生成信息摘要的无监督方法。此外，我们还引入了一种专门用于视频摘要的创新评估流程。实验结果表明，我们的无需训练的框架优于现有的无监督方法，并与最先进的监督方法取得竞争性结果。

增强上下文感知的视频摘要

Enhancing Video Summarization with Context Awareness

Our research endeavors to advance the concept of responsible artificial
intelligence (AI), a topic of increasing importance within EU policy
discussions. The EU has recently issued several publications emphasizing the
necessity of trust in AI, underscoring the dual nature of AI as both a
beneficial tool and a potential weapon. This dichotomy highlights the urgent
need for international regulation. Concurrently, there is a need for frameworks
that guide companies in AI development, ensuring compliance with such
regulations. Our research aims to assist lawmakers and machine learning
practitioners in navigating the evolving landscape of AI regulation,
identifying focal areas for future attention. This paper introduces a
comprehensive and, to our knowledge, the first unified definition of
responsible AI. Through a structured literature review, we elucidate the
current understanding of responsible AI. Drawing from this analysis, we propose
an approach for developing a future framework centered around this concept. Our
findings advocate for a human-centric approach to Responsible AI. This approach
encompasses the implementation of AI methods with a strong emphasis on ethics,
model explainability, and the pillars of privacy, security, and trust.

我们的研究旨在推进负责任的人工智能（AI）的概念，这是欧盟政策讨论中日益重要的主题。我们提出了一种全面且目前为止我们所知的第一个负责任 AI 的统一定义。通过结构化文献综述，我们阐明了关于负责任 AI 的当前理解，并基于该分析提出了未来以此概念为中心的框架发展方法。我们的发现支持以人为中心的负责任 AI 方法，其中包括以伦理、模型可解释性以及隐私、安全和信任为支柱的 AI 方法的实施。