  In this work we present a novel task of understanding unintentional human
activities in videos. We formalize this problem as a reasoning task under
zero-shot scenario, where given a video of an unintentional activity we want to
know why it transitioned from intentional to unintentional. We first evaluate
the effectiveness of current state-of-the-art Large Multimodal Models on this
reasoning task and observe that they suffer from hallucination. We further
propose a novel prompting technique,termed as Dream of Thoughts (DoT), which
allows the model to navigate through hallucinated thoughts to achieve better
reasoning. To evaluate the performance on this task, we also introduce three
different specialized metrics designed to quantify the models reasoning
capability. We perform our experiments on two different datasets, OOPs and
UCF-Crimes, and our findings show that DOT prompting technique is able to
outperform standard prompting, while minimizing hallucinations.
