InternVideo-Ego4D: A Pack of Champion Solutions to Ego4D Challenges
This work provides a practical solution for improving ego-centric video analysis, but it is incremental as it applies an existing foundation model to new tasks.
The authors adapted the InternVideo foundation model to five ego-centric video understanding tasks in the Ego4D challenge, achieving performance that surpassed baseline methods and previous champions.
In this report, we present our champion solutions to five tracks at Ego4D challenge. We leverage our developed InternVideo, a video foundation model, for five Ego4D tasks, including Moment Queries, Natural Language Queries, Future Hand Prediction, State Change Object Detection, and Short-term Object Interaction Anticipation. InternVideo-Ego4D is an effective paradigm to adapt the strong foundation model to the downstream ego-centric video understanding tasks with simple head designs. In these five tasks, the performance of InternVideo-Ego4D comprehensively surpasses the baseline methods and the champions of CVPR2022, demonstrating the powerful representation ability of InternVideo as a video foundation model. Our code will be released at https://github.com/OpenGVLab/ego4d-eccv2022-solutions