  This report describes our submission called "TarHeels" for the Ego4D: Object
State Change Classification Challenge. We use a transformer-based video
recognition model and leverage the Divided Space-Time Attention mechanism for
classifying object state change in egocentric videos. Our submission achieves
the second-best performance in the challenge. Furthermore, we perform an
ablation study to show that identifying object state change in egocentric
videos requires temporal modeling ability. Lastly, we present several positive
and negative examples to visualize our model's predictions. The code is
publicly available at: https://github.com/md-mohaiminul/ObjectStateChange
