  Multimodal large language models (MLLMs) are flourishing, but mainly focus on
images with less attention than videos, especially in sub-fields such as prompt
engineering, video chain-of-thought (CoT), and instruction tuning on videos.
Therefore, we try to explore the collection of CoT datasets in videos to lead
to video OpenQA and improve the reasoning ability of MLLMs. Unfortunately,
making such video CoT datasets is not an easy task. Given that human annotation
is too cumbersome and expensive, while machine-generated is not reliable due to
the hallucination issue, we develop an automatic annotation tool that combines
machine and human experts, under the active learning paradigm. Active learning
is an interactive strategy between the model and human experts, in this way,
the workload of human labeling can be reduced and the quality of the dataset
can be guaranteed. With the help of the automatic annotation tool, we strive to
contribute three datasets, namely VideoCoT, TopicQA, TopicCoT. Furthermore, we
propose a simple but effective benchmark based on the collected datasets, which
exploits CoT to maximize the complex reasoning capabilities of MLLMs. Extensive
experiments demonstrate the effectiveness our solution.
