1 research outputs found
A Semantics-Guided Class Imbalance Learning Model for Zero-Shot Classification
Zero-Shot Classification (ZSC) equips the learned model with the ability to
recognize the visual instances from the novel classes via constructing the
interactions between the visual and the semantic modalities. In contrast to the
traditional image classification, ZSC is easily suffered from the
class-imbalance issue since it is more concerned with the class-level knowledge
transfer capability. In the real world, the class samples follow a long-tailed
distribution, and the discriminative information in the sample-scarce seen
classes is hard to be transferred to the related unseen classes in the
traditional batch-based training manner, which degrades the overall
generalization ability a lot. Towards alleviating the class imbalance issue in
ZSC, we propose a sample-balanced training process to encourage all training
classes to contribute equally to the learned model. Specifically, we randomly
select the same number of images from each class across all training classes to
form a training batch to ensure that the sample-scarce classes contribute
equally as those classes with sufficient samples during each iteration.
Considering that the instances from the same class differ in class
representativeness, we further develop an efficient semantics-guided feature
fusion model to obtain discriminative class visual prototype for the following
visual-semantic interaction process via distributing different weights to the
selected samples based on their class representativeness. Extensive experiments
on three imbalanced ZSC benchmark datasets for both the Traditional ZSC (TZSC)
and the Generalized ZSC (GZSC) tasks demonstrate our approach achieves
promising results especially for the unseen categories those are closely
related to the sample-scarce seen categories