Hackernoon logoTop 10 Computer Vision Papers of 2020 by@whatsai

Top 10 Computer Vision Papers of 2020

Louis Bouchard Hacker Noon profile picture

@whatsaiLouis Bouchard

I explain Artificial Intelligence terms and news to non-experts.

Here is my list of the top 10 most interesting computer vision research papers of 2020.

In short, it is basically a curated list of the latest breakthroughs in AI and CV with a clear video explanation, link to a more in-depth article, and code (if applicable).

Enjoy the video, and let me know if I missed any important papers in the comments, or by contacting me directly on LinkedIn!

Watch the video

References

The top 10 computer vision papers in 2020 with video demos, articles, code, and paper reference.

Full list: https://pub.towardsai.net/top-10-computer-vision-papers-2020-aa606985f688

The GitHub repository: https://github.com/louisfb01/Top-10-Computer-Vision-Papers-2020

Watch the AI Rewind 2020: https://youtu.be/DHBclF-8KwE

Video Transcript

00:00

computer vision the goal of computer

00:02

vision is to understand the content of

00:05

images

00:05

while it's trivial for humans it's a

00:08

whole other deal for a computer

00:10

and therefore it's one of the biggest

00:12

fields involving machine learning

00:14

basically it involves developing methods

00:17

that attempt to reproduce the capability

00:19

of human vision

00:20

which is the perfect challenge for

00:22

machine learning since

00:24

it already tries to reproduce the way

00:26

humans learn

00:27

that is why machine learning and deep

00:29

learning is so

00:30

tied up with computer vision such

00:33

applications may involve extracting an

00:35

object from the image

00:36

a text description a three-dimensional

00:39

model and so on

00:40

in short computer vision is a huge field

00:43

of study where we try to give

00:45

vision to computers using cameras basic

00:48

algorithms

00:49

filters and much more it has been

00:51

improved a lot recently

00:53

using machine learning algorithms to

00:55

achieve its goal of

00:56

understanding images

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