  Generalized Zero-Shot Learning (GZSL) has emerged as a pivotal research
domain in computer vision, owing to its capability to recognize objects that
have not been seen during training. Despite the significant progress achieved
by generative techniques in converting traditional GZSL to fully supervised
learning, they tend to generate a large number of synthetic features that are
often redundant, thereby increasing training time and decreasing accuracy. To
address this issue, this paper proposes a novel approach for synthetic feature
selection using reinforcement learning. In particular, we propose a
transformer-based selector that is trained through proximal policy optimization
(PPO) to select synthetic features based on the validation classification
accuracy of the seen classes, which serves as a reward. The proposed method is
model-agnostic and data-agnostic, making it applicable to both images and
videos and versatile for diverse applications. Our experimental results
demonstrate the superiority of our approach over existing feature-generating
methods, yielding improved overall performance on multiple benchmarks.
