This paper evaluates the state of zero-shot learning (ZSL) by introducing a new benchmark and dataset, analyzing various methods, and discussing the limitations of current approaches. The authors argue that the lack of a standardized benchmark has led to inconsistent results and flawed evaluations. They propose a unified evaluation protocol, a new dataset (AWA2), and compare a wide range of ZSL and generalized ZSL (GZSL) methods. The paper highlights the importance of using a consistent evaluation protocol, avoiding pre-training on test classes, and evaluating methods on both seen and unseen classes. It also discusses the challenges of zero-shot learning, such as the need for robust feature extraction and the difficulty of obtaining labeled data for rare classes. The authors evaluate the performance of various methods on multiple datasets, including AWA1, AWA2, SUN, CUB, and ImageNet, and find that some methods perform better on certain datasets than others. The paper concludes that while ZSL has made progress, there are still significant challenges to overcome, and further research is needed to improve the accuracy and generalization of these methods.This paper evaluates the state of zero-shot learning (ZSL) by introducing a new benchmark and dataset, analyzing various methods, and discussing the limitations of current approaches. The authors argue that the lack of a standardized benchmark has led to inconsistent results and flawed evaluations. They propose a unified evaluation protocol, a new dataset (AWA2), and compare a wide range of ZSL and generalized ZSL (GZSL) methods. The paper highlights the importance of using a consistent evaluation protocol, avoiding pre-training on test classes, and evaluating methods on both seen and unseen classes. It also discusses the challenges of zero-shot learning, such as the need for robust feature extraction and the difficulty of obtaining labeled data for rare classes. The authors evaluate the performance of various methods on multiple datasets, including AWA1, AWA2, SUN, CUB, and ImageNet, and find that some methods perform better on certain datasets than others. The paper concludes that while ZSL has made progress, there are still significant challenges to overcome, and further research is needed to improve the accuracy and generalization of these methods.