  Temporally localizing the presence of object states in videos is crucial in
understanding human activities beyond actions and objects. This task has
suffered from a lack of training data due to object states' inherent ambiguity
and variety. To avoid exhaustive annotation, learning from transcribed
narrations in instructional videos would be intriguing. However, object states
are less described in narrations compared to actions, making them less
effective. In this work, we propose to extract the object state information
from action information included in narrations, using large language models
(LLMs). Our observation is that LLMs include world knowledge on the
relationship between actions and their resulting object states, and can infer
the presence of object states from past action sequences. The proposed
LLM-based framework offers flexibility to generate plausible pseudo-object
state labels against arbitrary categories. We evaluate our method with our
newly collected Multiple Object States Transition (MOST) dataset including
dense temporal annotation of 60 object state categories. Our model trained by
the generated pseudo-labels demonstrates significant improvement of over 29% in
mAP against strong zero-shot vision-language models, showing the effectiveness
of explicitly extracting object state information from actions through LLMs.
