A new visual recognition approach improved a machine learning technique’s ability to both identify an object and how it is oriented in space, according to a study presented in October at the European Conference on Computer Vision in Milan, Italy.
Self-supervised learning is a machine learning approach that trains on unlabeled data, extending generalizability to real-world data. While it excels at identifying objects, a task called semantic classification, it may struggle to recognize objects in new poses.
This weakness quickly becomes a problem in situations like autonomous vehicle navigation, where an algorithm must assess whether an approaching car is a head-on collision threat or side-oriented and just passing by.
Leave a reply