But while one's own face is not frequently visible, their hands are: in fact, hands are among the most common objects in one's own field of view.
Human infants have the remarkable ability to learn the associations between object names and visual objects from inherently ambiguous experiences.
One-shot fine-grained visual recognition often suffers from the problem of training data scarcity for new fine-grained classes.
Inspired by the remarkable ability of the infant visual learning system, a recent study collected first-person images from children to analyze the `training data' that they receive.
We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.
One of the time-consuming routine work for a radiologist is to discern anatomical structures from tomographic images.
Our work demonstrates the potential for performing free-space segmentation without tedious and costly manual annotation, which will be important for adapting autonomous driving systems to different types of vehicles and environments
CNNs eliminate the need for manually designing features and separation rules, but require a large amount of annotated training data.