EgoVideo: Exploring Egocentric Foundation Model and Downstream Adaptation
<|reference_start|>EgoVideo: Exploring Egocentric Foundation Model and Downstream Adaptation: In this report, we present our solutions to the EgoVis Challenges in CVPR 2024, including five tracks in the Ego4D challenge and three tracks in the EPIC-Kitchens challenge. Building upon the video-language two-tower model and leveraging our meticulously organized egocentric video data, we introduce a novel foundation model called EgoVideo. This model is specifically designed to cater to the unique characteristics of egocentric videos and provides strong support for our competition submissions. In the Ego4D challenges, we tackle various tasks including Natural Language Queries, Step Grounding, Moment Queries, Short-term Object Interaction Anticipation, and Long-term Action Anticipation. In addition, we also participate in the EPIC-Kitchens challenge, where we engage in the Action Recognition, Multiple Instance Retrieval, and Domain Adaptation for Action Recognition tracks. By adapting EgoVideo to these diverse tasks, we showcase its versatility and effectiveness in different egocentric video analysis scenarios, demonstrating the powerful representation ability of EgoVideo as an egocentric foundation model. Our codebase and pretrained models are publicly available at https://github.com/OpenGVLab/EgoVideo.<|reference_end|>
@article{pei2024egovideo:,
    title={EgoVideo: Exploring Egocentric Foundation Model and Downstream
  Adaptation},
    author={Baoqi Pei, Guo Chen, Jilan Xu, Yuping He, Yicheng Liu, Kanghua Pan,
  Yifei Huang, Yali Wang, Tong Lu, Limin Wang, Yu Qiao},
    journal={arXiv preprint arXiv:2406.18070},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18070},
    primaryClass={cs.CV}
}