Many computer systems that people interact with on a daily basis require knowledge about certain aspects of the world, or models, to work. These systems have to be trained, often needing to learn how to recognize objects .
Many computer systems that people interact with on a daily basis require knowledge about certain aspects of the world, or models, to work. These systems have to be trained, often needing to learn how to recognize objects from video or image data. This data frequently contains superfluous content that reduces the accuracy of models. So, researchers found a way to incorporate natural hand gestures into the teaching process. This way, users can more easily teach machines about objects, and the machines can also learn more effectively.
You’ve probably heard the term machine learning before, but are you familiar with machine teaching? Machine learning is what happens behind the scenes when a computer uses input data to form models that can later be used to perform useful functions. But machine teaching is the somewhat less explored part of the process, which deals with how the computer gets its input data to begin with.
In the case of visual systems, for example ones that can recognize objects, people need to show objects to a computer so it can learn about them. But there are drawbacks to the ways this is typically done that researchers from the University of Tokyo’s Interactive Intelligent Systems Laboratory sought to improve.
«In a typical object training scenario, people can hold an object up to a camera and move it around so a computer can analyze it from all angles to build up a model,» said graduate student Zhongyi Zhou.
