  Anticipating object state changes in images and videos is a challenging
problem whose solution has important implications in vision-based scene
understanding, automated monitoring systems, and action planning. In this work,
we propose the first method for solving this problem. The proposed method
predicts object state changes that will occur in the near future as a result of
yet unseen human actions. To address this new problem, we propose a novel
framework that integrates learnt visual features that represent the recent
visual information, with natural language (NLP) features that represent past
object state changes and actions. Leveraging the extensive and challenging
Ego4D dataset which provides a large-scale collection of first-person
perspective videos across numerous interaction scenarios, we introduce new
curated annotation data for the object state change anticipation task (OSCA),
noted as Ego4D-OSCA. An extensive experimental evaluation was conducted that
demonstrates the efficacy of the proposed method in predicting object state
changes in dynamic scenarios. The proposed work underscores the potential of
integrating video and linguistic cues to enhance the predictive performance of
video understanding systems. Moreover, it lays the groundwork for future
research on the new task of object state change anticipation. The source code
and the new annotation data (Ego4D-OSCA) will be made publicly available.
