在2024世界经济论坛的一次会谈中，图灵奖得主、Meta首席AI科学家Yann LeCun表示，对于如何让AI理解视频数据这一问题，目前尚未有明确的答案。但他强调，广泛应用于处理视频的生成模型并不适合，新的模型应当学会在抽象的表征空间中进行预测，而非像素空间。
LeCun指出，当前AI在处理视频数据时，面临着从像素级别理解信息到抽象概念转换的挑战。他认为，生成模型虽然在图像处理方面表现出色，但在视频领域却难以发挥同样的效果。这是因为视频不仅仅是一系列静态图像的集合，它还包含了时间序列的信息，这是生成模型难以捕捉的。
他强调，为了使AI更好地理解视频，需要开发出能够在更高层次上进行操作的模型，这些模型应该能够从视频的连续帧中抽象出关键信息，并在抽象的表征空间中进行预测和分析。这样的模型将有助于AI在视频内容的理解、分析和生成方面取得重大进展。
在AI领域，视频处理一直是一个难题。LeCun的这番言论，不仅为我们指出了现有模型的局限性，也为未来的研究方向提供了宝贵的启示。
News content: At the 2024 World Economic Forum, Yann LeCun, a Turing Award winner and the chief AI scientist at Meta, expressed his views on how AI can understand video data. He stated that there is no clear answer to this question yet, but it is certain that the generative models widely used for video processing are not suitable. Instead, new models should learn to predict in the abstract representational space, rather than in the pixel space.
LeCun pointed out that current AI faces challenges in converting information from pixel-level understanding to abstract concepts when processing video data. He believes that while generative models excel in image processing, they struggle to perform effectively in the video domain. This is because videos are not just collections of static images, but also contain temporal information that is difficult for generative models to capture.
He emphasized that in order for AI to better understand videos, it is necessary to develop models that can operate at a higher level, abstracting key information from consecutive frames in the video and predicting and analyzing in the abstract representational space. Such models are expected to greatly advance AI’s capabilities in understanding, analyzing, and generating video content.
Video processing has always been a challenge in the field of AI. LeCun’s remarks not only highlight the limitations of existing models but also provide valuable insights for future research directions.
【来源】https://mp.weixin.qq.com/s/sAWFkcTFfZVJ_oLKditqVA
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