标题：中山大学提出全新时空知识嵌入框架，刷新视频场景图生成任务SOTA
中山大学的研究团队近日在人工智能领域取得了重大突破，他们提出了一个全新的时空知识嵌入框架，这一研究成果已经在顶级期刊《Transactions on Image Processing (TIP)》的2024年刊上发表。
在视频场景图生成任务中，中山大学的研究团队提出的新框架刷新了最新的State-of-the-Art（SOTA）标准。这一框架的主要创新点在于，它将时空知识有效地嵌入到模型中，使得模型能够更好地理解和处理视频中的场景信息。
据了解，视频场景图生成任务是计算机视觉领域的一个重要研究方向，其主要目标是从视频中提取出有意义的场景信息，并将其转化为一张或多张静态的场景图。这一任务对于视频理解、视频检索、视频摘要等应用具有重要的意义。
中山大学的研究团队在这一领域的研究工作已经得到了国际同行的广泛认可。他们的这一最新研究成果不仅将推动视频场景图生成任务的发展，也将为其他相关领域的研究提供重要的参考和启示。
英语如下：
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“News Headline: Sun Yat-sen University Breaks New Record in Video ====
“News Headline: Sun Yat-sen University Breaks New Record in Video Scene Graph Generation, State-of-the-Art with Temporal-Spatial Knowledge Embedding Framework at TIP’24
Keywords: Sun Yat-sen University, Temporal-Spatial Knowledge Embedding, Video Scene Graph Generation
News Content: Title: Sun Yat-sen University Proposes a New Temporal-Spatial Knowledge Embedding Framework, Resetting the State-of-the-Art (SOTA) Standard in Video Scene Graph Generation
Sun Yat-sen University’s research team has recently achieved a major breakthrough in the field of artificial intelligence. They have proposed a new temporal-spatial knowledge embedding framework, which has been published in the top journal “Transactions on Image Processing (TIP)” in its 2024 edition.
In the task of video scene graph generation, the new framework proposed by Sun Yat-sen University’s research team has reset the latest State-of-the-Art (SOTA) standard. The main innovation of this framework lies in effectively embedding temporal-spatial knowledge into the model, enabling the model to better understand and process scene information in videos.
It is understood that video scene graph generation is an important research direction in the field of computer vision. Its main goal is to extract meaningful scene information from videos and convert it into one or more static scene graphs. This task is of great significance for applications such as video understanding, video retrieval, and video summarization.
The research team at Sun Yat-sen University has received wide recognition from international peers in this field. Their latest research achievement will not only promote the development of video scene graph generation tasks but also provide important references and insights for research in other related fields.”
【来源】https://www.36kr.com/p/2601576809806473
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