Join Karol Zak on a tour of video action detection using deep learning using 3DCNNs. Karol is going to cover off a breakthrough paper from 2015 “Learning spatiotemporal features with 3d convolutional networks”. 3D convnets are conceptually very easy to use and understand, they work like normal 2D CNNs but they use the third (depth) dimension to capture the time domain. Several large video action datasets have appeared on the scene too which have significantly democratised the practice i.e. Sports1M, Kinetics.

Karol’s style is very practical and hands-on and like last time will demonstrate the models working live in a Jupyter notebook and talk to some of his experience with video action detection.

https://www.youtube.com/watch?v=Ty-gww6GwHY

Paper link; https://arxiv.org/abs/1412.0767

Hi guys,

I’ll start my last year in my master’s degree and I’ve got an internship to do for 6 months in 2020. This year is specialized
in AI and I’ve already done some basic projects in DP. However, for my internship, I wanna be ambitious and try to look for some goods labs in order to get a PhD after.

But I don’t know really what kind of projects that I can do to present my lvl in CS. I know it’s important to have a good Github profile, but what do people expect from a student at my level ? Any idea from where to start ? I’ve got the feeling that doing things that have already been done isn’t really worth it, I’m a bit lost.

Thank you for you time

I published a multipart “Tutorial on Graph Neural Networks for Computer Vision and Beyond” starting from some basics [1], then an overview explaining several important methods [2] and a separate post on spectral convolution [3].

I know there are a lot of blog posts on graph networks already, but in my tutorial I tried to explain key (and sometimes complicated) ideas in very simple terms from a computer vision perspective, so it should be good for those with a computer vision and machine learning background. I provide detailed Python and PyTorch examples to clarify differences between methods.

I wasn’t sure if to publish it here due to this discussion (Regarding beginner’s guides: https://www.reddit.com/r/MachineLearning/comments/co37ut/regarding_beginners_guides/ ), but hopefully it will be appreciated here. Otherwise, feel free to downvote or remove.

Any questions or feedback is very welcome, especially, if you notice some mistakes or confusing info.

[1] Part 1 of the Tutorial: convolution on graphs and differences between simple fully-connected neural networks (MLPs) and graph networks: https://medium.com/@BorisAKnyazev/tutorial-on-graph-neural-networks-for-computer-vision-and-beyond-part-1-3d9fada3b80d

[2] Anisotropic, Dynamic, Spectral and Multiscale Filters Defined on Graphs: https://towardsdatascience.com/tutorial-on-graph-neural-networks-for-computer-vision-and-beyond-part-2-be6d71d70f49

[3] Spectral Graph Convolution Explained and Implemented Step By Step: https://towardsdatascience.com/spectral-graph-convolution-explained-and-implemented-step-by-step-2e495b57f801

If we have a single large dataset D, and partitioned into A and B and put on devices.

I make two replicas of M¹ and M² from a same model M and also with same initial weights(same seed). I put these on the above two devices and train them separately.

How do I combine(if it’s the word) these two models’ experience into a single one(like a model which has learned the entire data D)?

My network is 1 Gbit ethernet and i am trying to use pytorch distributed training on two 8-gpu servers. Training procedure is simple classification objective with feed-forward network. I experience significant slowdown in comparison with single 8-gpu server training. Also “nload” tool shows full bandwidth usage even for small model (resnet18).

Is my network too slow for distributed training? If it is, what bandwidth (in Gbit/s) do I need to train heavy models like resnet101?

Hey everyone,

I’m a Computer Science(BS) senior looking for ideas for my capstone project that gives care to the scope of a semester (until about early December, 15ish weeks) . The thing about this capstone is that while it needs to be finished by that time, if there is significant room for expansion, it can be improved upon in my 2nd semester capstone. Other relevant information is that I’m going to be working on a video game throughout the semester for a separate class (nothing complex due to time). Requirements for the capstone are pretty relaxed so long as the project exists for a clear purpose.

​

Knowing that, do you guys think that within a semester it is feasible to design a manageable project which would then still be relevant (for a 2nd semester continuation) to a small game I can make alongside the capstone?

​

I’d appreciate any feedback on your minds! Thanks

The project works by employing a siamese network to predict what you say and performs the respective action. Unlike traditional neural networks, this method does not require a large dataset to train on, powerful hardware resources or a long training time. You can get this up and running typically in 10-15 minutes and you do not need a GPU to run this program.

Github link :- https://github.com/andohuman/Shadowcol

I’ve provided very detailed installation instructions in the repo and instructions on how to train your own model with your own commands to aid your gaming experience.

I’d also like to mention that this project is still under development and there might be a few bugs. Please DO NOT ATTEMPT TO PLAY ANY COMPETITIVE GAMES WITH THIS (Had to learn this the hard way after dying a couple of times in overwatch).

The typical use case I see for this is mapping hard-to-reach keys to your voice instead. Or use your voice commands to open/close applications in your PC. I’ll leave you to your imagination.

Play with the code and let me know what you guys think, and if you have any suggestions I’d be glad to hear it.

Special thanks to u/chaosparrot and u/jonnor for their insights !