Object Recognition with Zero-shot Learning

OpenAlex İngilizce

Zero-shot learning aims to classify unseen class examples. It gained popularity in applications where examples for each category are limited. The main issue to consider is transferring information from seen classes to unseen classes via mapping image space to semantic space. Therefore, mapping from image space to semantic space is at the core of the learning process. In this work, Google’s Word2vec were used for semantic space. Total of 20 classes, 15 for training and 5 for zero-shot classes were chosen from Visual Gnome dataset. We have achieved 0.71 accuracy for top-5 classes.