CVJul 22, 2022
Video Swin Transformers for Egocentric Video Understanding @ Ego4D Challenges 2022
arXiv:2207.11329v18 citationsh-index: 28
Originality Synthesis-oriented
AI Analysis
This work addresses video understanding problems for egocentric AI applications, but it is incremental as it applies an existing method to new data.
The authors tackled the tasks of Point-of-No-Return temporal localization and Object State Change Classification in egocentric video understanding using Video Swin Transformers, achieving competitive performance on the Ego4D Challenges 2022.
We implemented Video Swin Transformer as a base architecture for the tasks of Point-of-No-Return temporal localization and Object State Change Classification. Our method achieved competitive performance on both challenges.