The AI Monthly Top 3 — June 2021
The 3 most interesting AI papers this month with video demos, short articles, code, and paper reference.
Here are the 3 most interesting research papers of the month, in case you missed any of them. It is a curated list of the latest breakthroughs in AI and Data Science by release date with a clear video explanation, link to a more in-depth article, and code (if applicable). Enjoy the read, and let me know if I missed any important papers in the comments, or by contacting me directly on LinkedIn!
If you’d like to read more research papers as well, I recommend you read my article where I share my best tips for finding and reading more research papers.
Paper #1:
DefakeHop: A Light-Weight High-Performance Deepfake Detector [1]
How to Spot a Deep Fake in 2021. Breakthrough US Army technology using artificial intelligence to find deepfakes.
While they seem like they’ve always been there, the very first realistic deepfake didn’t appear until 2017. It went from the first-ever resembling fake images automatically generated to today’s identical copy of someone on videos, with sound.
The reality is that we cannot see the difference between a real video or picture and a deepfake anymore. How can we tell what’s real from what isn’t? How can audio files or video files be used in court as proof if an AI can entirely generate them? Well, this new paper may provide answers to these questions. And the answer here may again be the use of artificial intelligence. The saying “I’ll believe it when I’ll see it” may soon change for “I’ll believe it when the AI tells me to believe it…”
Watch the video
A short read version
Paper #2:
Barbershop: GAN-based Image Compositing using Segmentation Masks [2]
This article is not about a new technology in itself. Instead, it is about a new and exciting application of GANs. Indeed, you saw the title, and it wasn’t clickbait. This AI can transfer your hair to see how it would look like before committing to the change…
Watch the video
A short read version
Code: https://github.com/ZPdesu/Barbershop
Paper #3:
TextStyleBrush: Transfer of text aesthetics from a single example [3]
This new Facebook AI model can translate or edit text directly in the image in your own language, following the same style!
Imagine you are on vacation in another country where you do not speak the language. You want to try out a local restaurant, but their menu is in the language you don’t speak. I think this won’t be too hard to imagine as most of us already faced this situation whether you see menu items or directions and you can’t understand what’s written. Well, in 2020, you would take out your phone and google translate what you see. In 2021 you don’t even need to open google translate anymore and try to write what you see one by one to translate it. Instead, you can simply use this new model by Facebook AI to translate every text in the image in your own language…
Watch the video
A short read version
Bonus Paper:
Animating Pictures with Eulerian Motion Fields [Bonus]
This model takes a picture, understands which particles are supposed to be moving, and realistically animates them in an infinite loop while conserving the rest of the picture entirely still creating amazing-looking videos like this one… [read the article to see it!]
Watch the video
A short read version
Code: https://eulerian.cs.washington.edu/
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References
[1] DeepFakeHop: Chen, Hong-Shuo, et al., (2021), “DefakeHop: A Light-Weight High-Performance Deepfake Detector.” ArXiv abs/2103.06929
[2] Peihao Zhu et al., (2021), Barbershop, https://arxiv.org/pdf/2106.01505.pdf
[3] Praveen Krishnan, Rama Kovvuri, Guan Pang, Boris Vassilev, and Tal Hassner, Facebook AI, (2021), ”TextStyleBrush: Transfer of text aesthetics from a single example”, https://scontent.fymq3-1.fna.fbcdn.net/v/t39.8562-6/10000000_944085403038430_3779849959048683283_n.pdf?_nc_cat=108&ccb=1-3&_nc_sid=ae5e01&_nc_ohc=Jcq0m5jBvK8AX9p0hND&_nc_ht=scontent.fymq3-1.fna&oh=ab1cc3f244468ca196c76b81a299ffa1&oe=60EF2B81
[Bonus] Holynski, Aleksander, et al. “Animating Pictures with Eulerian Motion Fields.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021., https://arxiv.org/abs/2011.15128