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Why Does Computer Vision Develop In a Faster Way Compared to NLP?

Both Computer Vision and NLP (natural language processing) have been good at tackling certain circumscribed tasks. Still, they are both progressing at a rather slow speed and the NLP field is even lesser than computer vision. Wondering why? Below, we have handpicked major reasons for faster computer vision advancing when compared to NLP.

So, Computer Vision matures faster because of:

  • Solid accuracy in problem-solving. Many basic computer vision issues, not to mention Object Detection and Face Recognition,  have been solved with solid accuracy.
  • Ubiquitous product coverage. Such big names like Facebook working in Facial Recognition and Google Goggles working in Object Detection have helped the computer vision technology become more mature.
  • Great interest in image technologies. We can notice that tech companies have been concentrating more on images than text.
  • Advances in hardware. Hardware, for example, depth cameras with greater pixels coverage, like the one in Kinect. Indeed, today we’ve got pretty decent cameras, which can easily separate a human being from the background.

Of course, we can’t say that the natural language processing has been standing still either. A lot has been done in the NLP field, and unlike computer vision, where the accuracy has been improved several times recently, NLP has always had 80-90% accuracy. Plus, the NLP community has been doing a good job of making huge annotated datasets capable to train supervised machine learning algorithms.

And still, the NLP field lacks such a huge attention of big tech companies the computer vision now boasts.

The Bottom Line

Both Computer Vision and NLP (natural language processing) have been doing a great job these days. But computer vision is advancing more rapidly in comparison with NLP, first of all, due to computer vision massive interest and support from Huge Tech Companies, like Facebook and Google. Let’s hope that recent advances in deep learning and recurrent nets may soon revolutionize NLP too. It is of vital importance to admit that neither Computer Vision and NLP are far from achieving human-level common sense, so there is still a great deal left to do in both directions. And if applied together, they have an unimaginable potential. By the way, together they’ve already become a novel interdisciplinary field that has gained a lot of attention so far and will definitely have in the future. 

Do you know any other reason why natural language processing is developing in a slower manner when compared to computer vision? We’d like to learn them too!

We will be happy to hear your thoughts

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