Masked Autoencoders for Egocentric Video Understanding @ Ego4D Challenge 2022
About
In this report, we present our approach and empirical results of applying masked autoencoders in two egocentric video understanding tasks, namely, Object State Change Classification and PNR Temporal Localization, of Ego4D Challenge 2022. As team TheSSVL, we ranked 2nd place in both tasks. Our code will be made available.
Jiachen Lei, Shuang Ma, Zhongjie Ba, Sai Vemprala, Ashish Kapoor, Kui Ren• 2022
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Temporal Attribution | Ego4D-M (test) | MAE (frames)48.96 | 3 |
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