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This report describes our submission called "TarHeels" for the Ego4D: ObjectState Change Classification Challenge. We use a transformer-based videorecognition model and leverage the Divided Space-Time Attention mechanism forclassifying object state change in egocentric videos. Our submission achievesthe second-best performance in the challenge. Furthermore, we perform anablation study to show that identifying object state change in egocentricvideos requires temporal modeling ability. Lastly, we present several positiveand negative examples to visualize our model's predictions. The code ispublicly available at: https://github.com/md-mohaiminul/ObjectStateChange

Object State Change Classification in Egocentric Videos using the Divided Space-Time Attention Mechanism