Learning the Compositional Spaces for Generalized Zero-shot Learning
It addresses the problem of classifying both seen and unseen classes in G-ZSL, which is an incremental improvement over previous methods.
The paper tackles Generalized Zero-shot Learning (G-ZSL) by proposing a novel space decomposition method that directly estimates and fine-tunes the decision boundary between seen and unseen classes, achieving state-of-the-art performances on multiple benchmarks.
This paper studies the problem of Generalized Zero-shot Learning (G-ZSL), whose goal is to classify instances belonging to both seen and unseen classes at the test time. We propose a novel space decomposition method to solve G-ZSL. Some previous models with space decomposition operations only calibrate the confident prediction of source classes (W-SVM [46]) or take target-class instances as outliers [49]. In contrast, we propose to directly estimate and fine-tune the decision boundary between the source and the target classes. Specifically, we put forward a framework that enables to learn compositional spaces by splitting the instances into Source, Target, and Uncertain spaces and perform recognition in each space, where the uncertain space contains instances whose labels cannot be confidently predicted. We use two statistical tools, namely, bootstrapping and Kolmogorov-Smirnov (K-S) Test, to learn the compositional spaces for G-ZSL. We validate our method extensively on multiple G-ZSL benchmarks, on which it achieves state-of-the-art performances.