Goal-oriented dialogue systems interact with humans to accomplish specific tasks. However, sometimes these systems fail to establish a common ground with users, leading to task failures. In such cases, it is crucial not to just end with failure but to correct and recover the dialogue to turn it into a success for building a robust goal-oriented dialogue system. Effective recovery from task failures in a goal-oriented dialogue involves not only successful recovery but also accurately understanding the situation of the failed task to minimize unnecessary interactions and avoid frustrating the user. In this study, we analyze the capabilities of GPT-4V in recovering failure tasks by comparing its performance with that of humans using Guess What?! Game. The results show that GPT-4V employs less efficient recovery strategies, such as asking additional unnecessary questions, than humans. We also found that while humans can occasionally ask questions that doubt the accuracy of the interlocutor’s answer during task recovery, GPT-4V lacks this capability.