How we teach computers to understand pictures | Fei Fei Li

章节 1：计算机视觉为何如此困难

📝 本节摘要：

本章介绍计算机视觉面临的核心挑战：机器虽然能够拍摄、生成和处理海量图像，却仍难以真正理解其中的内容。主讲人通过儿童识图、自驾汽车、盲人视觉辅助、无人机监测和安全摄像头等例子，说明视觉理解远比获取像素复杂，人类社会在智能机器仍然“看不见”的情况下，整体上依旧是盲的。

[原文] [Fei-Fei Li]: Okay, that's a cat sitting in a bed. The boy is petting the elephant. Those are people that are going on an airplane. That's a big airplane.

[译文] [Fei-Fei Li]: 好，那是一只坐在床上的猫。男孩正在抚摸大象。那些人要登上飞机。那是一架很大的飞机。

[原文] [Fei-Fei Li]: This is a three-year-old child describing what she sees in a series of photos. She might still have a lot to learn about this world, but she's already an expert at one very important task: to make sense of what she sees. Our society is more technologically advanced than ever. We send people to the moon, we make phones that talk to us or customize radio stations that can play only music we like.

[译文] [Fei-Fei Li]: 这是一个三岁孩子在描述她从一组照片中看到的内容。她对这个世界还有很多东西要学习，但在一项非常重要的任务上，她已经是专家了：理解自己所看到的一切。我们的社会比以往任何时候都更加先进。我们能把人送上月球，能制造会和我们交谈的手机，也能定制只播放我们喜欢音乐的电台。

[原文] [Fei-Fei Li]: Yet, our most advanced machines and computers still struggle at this task. So I'm here today to give you a progress report on the latest advances in our research in computer vision, one of the most frontier and potentially revolutionary technologies in computer science. Yes, we have prototyped cars that can drive by themselves, but without smart vision, they cannot really tell the difference between a crumpled paper bag on the road, which can be run over, and a rock that size, which should be avoided. We have made fabulous megapixel cameras, but we have not delivered sight to the blind.

[译文] [Fei-Fei Li]: 然而，我们最先进的机器和计算机在这项任务上仍然举步维艰。所以今天我想向大家介绍计算机视觉研究的最新进展。计算机视觉是计算机科学中最前沿、也最具革命潜力的技术之一。没错，我们已经制造出了能够自动驾驶的汽车原型，但如果没有智能视觉，它们仍然无法真正分辨：路上一个可以直接碾过去的皱纸袋，和一个同样大小、应该避开的石头。我们制造出了性能惊人的百万像素相机，却还没有真正让盲人获得视觉。

[原文] [Fei-Fei Li]: Drones can fly over massive land, but don't have enough vision technology to help us to track the changes of the rainforests. Security cameras are everywhere, but they do not alert us when a child is drowning in a swimming pool. Photos and videos are becoming an integral part of global life. They're being generated at a pace that's far beyond what any human, or teams of humans, could hope to view, and you and I are contributing to that at this TED. Yet our most advanced software is still struggling at understanding and managing this enormous content.

[译文] [Fei-Fei Li]: 无人机可以飞越广袤的土地，却没有足够先进的视觉技术来帮助我们追踪热带雨林的变化。摄像头无处不在，但当孩子在游泳池里溺水时，它们并不会提醒我们。照片和视频正成为全球生活不可或缺的一部分。它们产生的速度远远超过任何个人或人类团队所能浏览的数量，而你我在这场 TED 演讲中也正在为此贡献内容。然而，我们最先进的软件在理解和管理这些海量内容时，仍然十分吃力。

[原文] [Fei-Fei Li]: So, in other words, collectively as a society, we're very much blind, because our smartest machines are still blind. "Why is this so hard?" you may ask. Cameras can take pictures like this one by converting light into a two-dimensional array of numbers known as pixels, but these are just lifeless numbers. They do not carry meaning in themselves. Just like to hear is not the same as to listen, to take pictures is not the same as to see, and by seeing, we really mean understanding. In fact, it took Mother Nature 540 million years of hard work to do this task, and much of that effort went into developing the visual processing apparatus of our brains, not the eyes themselves.

[译文] [Fei-Fei Li]: 换句话说，作为一个整体，我们的社会其实相当于处于失明状态，因为我们最聪明的机器仍然看不见。你可能会问：“这为什么这么难？”相机可以像拍摄这张照片一样，把光转换成由数字组成的二维阵列，也就是像素，但这些只是没有生命的数字，它们本身并不携带意义。就像听见不等于倾听，拍下照片也不等于看见；而我们所说的“看见”，真正的含义是理解。事实上，大自然花了五亿四千万年的艰苦努力才完成这项任务，而其中很大一部分工作都用来发展我们大脑中的视觉处理系统，而不是眼睛本身。