Semantic-aligned reinforced attention model for zero-shot learning

Zaiquan Yang, Yuqi Zhang, Yuxin Du, Chao Tong

Abstract: Highlights•Propose a ZSL model to mimic human cognitive process by exploiting the reinforced attention from both space and channel.•Develop an efficient loss for constraint on feature map for better interpretation.•Achieve superior or competitive performance on widely used standard benchmarks.