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精华 发表在 汉化 01-02 11:32:19  来自PC 复制链接 手机看帖扫一扫!手机看帖更爽 1182 32854

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Beauty is in the eye of the beholder, or so the saying goes, and the same is often true when trying to pick out a perfect photography. Say you’ve got ten relatively similar shots of a loved one, family pet, or a stunning landscape – which one is the perfect shot and, crucially, why?

美在每个人的眼中都不一样,就跟你挑选你认为美的照片一样,不同的人总会找出不同的美。如果你有很多相似的、可爱的、美丽的照片,让你找出一张最完美的,你会怎么选择呢?

It’s a tough question to answer as there are multiple factors at play. It could be the shot which is the most competent, with no sign of any pesky blur or noise, but, on the other hand, it could also be the shot which catches the light in a way that makes it more appealing than the rest, even if it isn’t technically the best of the bunch.

这确实是一个比较棘手的问题,毕竟有好多因素会影响我们的选择。

Even if we’re not aware of it, the human brain tends to strike a balance between technical quality and aesthetic preference when judging photos. This means that even amateur photographers can pick out their favorite shot from a similar batch.

人们往往会根据照片的拍摄技术质量和个人的审美来选择一张自己最喜欢的。业余摄像爱好者也可以如此。

But what if artificial intelligence could select the ‘best photo’ for us? A team of Google researchers have attempted to do just that with an AI model dubbed Neural Image Assessment (NIMA).

但是如果人工智能能够帮我们选择一张最好看的图片呢?今日谷歌的研究团队试图使用NIMA人工智能系统来实现。

By now we’re all familiar with AI features baked into current smartphone camera suites which identify objects within each photo. NIMA goes one step further, using deep learning techniques to train a convolutional neural network (CNN) that can rate an image not just on its technical quality, but also how likely its overall aesthetic will appeal to the human eye.

到目前为止,我们对智能手机里相机中的AI功能比较熟悉,这些功能可以帮助识别每张照片中的物体。而NIMA更进一步,其使用深度学习技术来训练卷积神经网络(CNN),不仅可以从技术角度上对照片评分,还可以评估照片整体上的美学对人眼的吸引力

Rather than categorizing an image as either high/low technical quality, NIMA uses a scoring system to rate the aesthetics of a photo on a scale of 1 to 10. Using this method, NIMA can examine each individual pixel for a technical assessment while also taking into account “semantic level characteristics associated with emotions and beauty in images.”

NIMA不是简单的将图像分类为高/低质量,而是使用评分系统以1至10的分值对照片的美学进行评分。使用这种方法,NIMA可以检查每个像素进行技术评估,同时会考虑到“图像中的情感以及和美丽相关的特征”。

As for the AI’s practical applications, it’s not hard to imagine a feature on a phone – perhaps in a future update fo the Google Pixel 2 – which selects the best photo without the user having to trawl through endless near-duplicates. The researchers also suggest that NIMA could “enable improved picture-taking with real-time feedback to the user,” and even help post-processing techniques produce “perceptually superior results”.

至于这个AI系统的实际应用,不难想象,在未来的手机上会出现一个功能 --- 选择最好的照片,用户不必在无尽的照片中纠结到底哪一张好看。 研究人员还提出,NIMA可以“为用户提供实时反馈,改善拍摄效果”,甚至可以帮助后期处理。

你认为谷歌的这套AI系统怎么样?你相信AI会帮你选择你心中最完美的照片吗?你希望用到小米7中吗?欢迎跟帖回复讨论。


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