Harvard and MIT researchers have demonstrated a way to build more powerful artificial visual systems, taking inspiration from screening techniques in molecular biology (a multitude of candidate organisms or compounds are screened in parallel to find those that have a particular property of interest).

“Reverse-engineering a biological visual system–a system with hundreds of millions of processing units–and building an artificial system that works the same way is a daunting task,” says David Cox, Principal Investigator of the Visual Neuroscience Group at the Rowland Institute at Harvard.

Rather than building a single model and seeing how well it could recognize visual objects, the team constructed thousands of candidate models, and screened for those that performed best on an object recognition task.

The resulting models outperformed a crop of state-of-the-art computer vision systems across a range of test sets, more accurately identifying a range of objects on random natural backgrounds with variation in position, scale, and rotation.

Using ordinary computer processing units, the effort would have required either years of time or millions of dollars of computing hardware. Instead, by harnessing modern graphics processing units (GPUs), the analysis was done in just one week, and at a small fraction of the cost.

This high-throughput approach could be applied to other areas of computer vision, such as face identification, object tracking, pedestrian detection for automotive applications, and gesture and action recognition. Moreover, as scientists understand better what components make a good artificial vision system, they can use these hints when studying real brains to understand them better as well.

“Reverse and forward engineering the brain is a virtuous cycle. The more we learn about one, the more we can learn about the other,” says Cox. “Tightly coupling experimental neuroscience and computer engineering holds the promise to greatly accelerate both fields.”

Source: Harvard and MIT news release