Two Minute Papers highlights the paper, “TossingBot: Learning to Throw Arbitrary Objects with Residual Physics.”
Two Minute Papers explores the paper “Learning Correspondence from the Cycle-Consistency of Time” in this video.
While readers of this blog may think that computer vision and neural networks have a long history together, the fact is: they don’t. Machine vision encompasses far more than Hot Dog or Not a Hot Dog. Here’s an interesting look at how deep learning has changed machine vision forever.
Underneath this hyperbole, however, describing the underlying science behind such concepts is more simple. In traditional machine vision systems, for example, it may be necessary to read a barcode on a part, judge its dimensions, or inspect it for flaws. To do so, systems integrators often use off-the-shelf softwarethat offers standard tools that can be deployed to determine a data matrix code, for example, or caliper tools set using graphical user interfaces to measure part dimensions.
Two Minute Papers takes a look at DeepMind’s recent paper on understanding 3D scenes. Watch the video to find out why this a big deal.
The human brain’s ability to recognize objects is remarkable. If you see under unusual lighting or from unexpected directions, there’s a good chance that your brain will still recognize it, and it’s considered an anamoly when it doesn’t. This robust and precise object recognition is a holy grail for artificial intelligence developers. How our brains do this, however, is still a mystery. Here’s an interesting article from MIT on how researchers may be on to something powerful in the computer vision space.
Think of feedforward DCNNs, and the portion of the visual system that first attempts to capture objects, as a subway line that runs forward through a series of stations. The extra, recurrent brain networks are instead like the streets above, interconnected and not unidirectional. Because it only takes about 200 ms for the brain to recognize an object quite accurately, it was unclear if these recurrent interconnections in the brain had any role at all in core object recognition.
Perhaps those recurrent connections are only in place to keep the visual system in tune over long periods of time. For example, the return gutters of the streets help slowly clear it of water and trash, but are not strictly needed to quickly move people from one end of town to the other. DiCarlo, along with lead author and CBMM postdoc Kohitij Kar, set out to test whether a subtle role of recurrent operations in rapid visual object recognition was being overlooked.
Lex Fridman of MIT demonstrates Driver Activity Recognition in a self-driving car by playing Black Betty on the guitar. Yes, you read that right. What a time to be alive, amirite?!
Computer vision is real and has quickly gone from cutting edge and into the mainstream. Here’s a great article on getting started with computer vision that’s worth checking out.
In 2012, AlexNet took first place at the ImageNet Large Scale Visual Recognition Challenge, marking the first time a convolutional neural network had won the image classification competition. One more factor that made this achievement much more significant is that AlexNet showed twice the accuracy than the second-place participant. […]