MULTI-VIEW EMBEDDING WITH SOFT-MAX BASED COMPATIBILITY FUNCTION FOR ZERO-SHOT LEARNING

摘要

Described is a system for multi-view embedding for object recognition. During operation, the system receives an input image and transforms raw data of objects in the image with corresponding labels into low-level features and high-level semantic representations of the labels. A trained object recognition model is generated by embedding the low-level features with multiple high-level semantic representations. The system then receives data of an unknown object and assigns a label to the unknown object using the trained object recognition model. Finally, a device can be controlled based on the label.