  Generalized Zero-Shot Learning (GZSL) aims to train a classifier that can
generalize to unseen classes, using a set of attributes as auxiliary
information, and the visual features extracted from a pre-trained convolutional
neural network. While recent GZSL methods have explored various techniques to
leverage the capacity of these features, there has been an extensive growth of
representation learning techniques that remain under-explored. In this work, we
investigate the utility of different GZSL methods when using different feature
extractors, and examine how these models' pre-training objectives, datasets,
and architecture design affect their feature representation ability. Our
results indicate that 1) methods using generative components for GZSL provide
more advantages when using recent feature extractors; 2) feature extractors
pre-trained using self-supervised learning objectives and knowledge
distillation provide better feature representations, increasing up to 15%
performance when used with recent GZSL techniques; 3) specific feature
extractors pre-trained with larger datasets do not necessarily boost the
performance of GZSL methods. In addition, we investigate how GZSL methods fare
against CLIP, a more recent multi-modal pre-trained model with strong zero-shot
performance. We found that GZSL tasks still benefit from generative-based GZSL
methods along with CLIP's internet-scale pre-training to achieve
state-of-the-art performance in fine-grained datasets. We release a modular
framework for analyzing representation learning issues in GZSL here:
https://github.com/uvavision/TV-GZSL
