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Computer Science > Computer Vision and Pattern Recognition

arXiv:2207.11814 (cs)
[Submitted on 24 Jul 2022 (v1), last revised 4 Jan 2023 (this version, v2)]

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

Authors:Md Mohaiminul Islam, Gedas Bertasius
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Abstract: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: this https URL
Comments: 2nd place winner, Ego4D challenge, CVPR 2022
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2207.11814 [cs.CV]
  (or arXiv:2207.11814v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2207.11814

Submission history

From: Md Mohaiminul Islam [view email]
[v1] Sun, 24 Jul 2022 20:53:36 UTC (6,331 KB)
[v2] Wed, 4 Jan 2023 12:04:20 UTC (6,331 KB)
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