93 citations · 427 across the 35 of their papers we have counts for
5 papers · 1 filter
InternVideo: General Video Foundation Models via Generative and Discriminative Learning
Yi Wang, Kunchang Li, Yizhuo Li +14
The foundation models have recently shown excellent performance on a variety of downstream tasks in computer vision. However, most existing vision foundation models simply focus on…
Exploring adaptation of VideoMAE for Audio-Visual Diarization & Social @ Ego4d Looking at me Challenge
Yinan He, Guo Chen
In this report, we present the transferring pretrained video mask autoencoders(VideoMAE) to egocentric tasks for Ego4d Looking at me Challenge. VideoMAE is the data-efficient pretr…
UniFormerV2: Spatiotemporal Learning by Arming Image ViTs with Video UniFormer
Kunchang Li, Yali Wang, Yinan He +4
Learning discriminative spatiotemporal representation is the key problem of video understanding. Recently, Vision Transformers (ViTs) have shown their power in learning long-term v…
InternVideo-Ego4D: A Pack of Champion Solutions to Ego4D Challenges
Guo Chen, Sen Xing, Zhe Chen +18
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, includin…
X-Learner: Learning Cross Sources and Tasks for Universal Visual Representation
Yinan He, Gengshi Huang, Siyu Chen +7
In computer vision, pre-training models based on largescale supervised learning have been proven effective over the past few years. However, existing works mostly focus on learning…