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.
翻译:摘要:通用零样本学习(GZSL)因其在识别训练中未出现的对象方面的能力而成为计算机视觉中至关重要的研究领域。尽管生成技术在将传统的GZSL转换为完全监督学习方面取得了重要进展,但它们往往会生成大量的合成特征,这些特征往往是冗余的,从而增加了训练时间并降低了准确性。为了解决这个问题,本文提出了一种使用增强学习进行合成特征选择的新方法。特别地,我们提出了一个基于Transformer的选择器,通过接近策略优化(PPO)进行训练,根据已见课程的验证分类准确性进行合成特征选择,这作为奖励。所提出的方法是模型不可知和数据不可知的,因此适用于图像和视频,并且可以用于各种应用。我们的实验结果表明,我们的方法优于现有的特征生成方法,在多个基准测试中产生了改进的整体性能。