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!

Paper #1:

An AI Software Able To Detect and Count Plastic Waste in the Ocean [1]

Odei Garcia-Garin et al. from the University of Barcelona have developed a deep learning-based algorithm able to detect and quantify floating garbage from aerial images. They also made a web-oriented application allowing users to identify these garbages, called floating marine macro-litter, or FMML, within images of the sea surface.

Watch the video

A short read version

An AI Software Able To Detect and Count Plastic Waste in the Ocean

Odei Garcia-Garin et al. from the University of Barcelona have developed a deep learning-based algorithm able to detect and quantify floating garbage from aerial images. They also made a web-oriented…

Towards AILouis (What’s AI) Bouchard

Code & web app: https://github.com/amonleong/MARLIT

Paper #2:

ShaRF: Take a Picture From a Real-Life Object, and Create a 3D Model of It [2]

Just imagine how cool it would be to just take a picture of an object and have it in 3D to insert in the movie or video game you are creating or in a 3D scene for an illustration.

Watch the video

A short read version

ShaRF: Take a Picture From a Real-Life Object, and Create a 3D Model of It

Neural scene representation from a single image is a really complex problem. The “end goal” is to be able to take a picture from a real-life object, and translate this picture into a 3D scene. It…

Towards AILouis (What’s AI) Bouchard

Project website and link to code for ShaRF: http://www.krematas.com/sharf/index.html

Paper #3:

GANsformers: Scene Generation with Generative Adversarial Transformers [3]

They basically leverage transformers’ attention mechanism in the powerful StyleGAN2 architecture to make it even more powerful!

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A short read version

Generative Adversarial Transformers: GANsformers Explained

Last week we looked at DALL-E, OpenAI’s most recent paper.It uses a similar architecture as GPT-3 involving transformers to generate an image from text. This is a super interesting and complex task…

Towards AILouis (What’s AI) Bouchard

Code: https://github.com/dorarad/gansformer

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References

[1] Odei Garcia-Garin et al., Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R, Environmental Pollution, https://doi.org/10.1016/j.envpol.2021.116490.

[2] Rematas, K., Martin-Brualla, R., and Ferrari, V., “ShaRF: Shape-conditioned Radiance Fields from a Single View”, (2021), https://arxiv.org/abs/2102.08860

[3] Drew A. Hudson and C. Lawrence Zitnick, Generative Adversarial Transformers, (2021)