108 citations · 791 across the 43 of their papers we have counts for
12 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…
MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistency
Mingye Xu, Mutian Xu, Tong He +4
Masked Modeling (MM) has demonstrated widespread success in various vision challenges, by reconstructing masked visual patches. Yet, applying MM for large-scale 3D scenes remains a…
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…
VideoPipe 2022 Challenge: Real-World Video Understanding for Urban Pipe Inspection
Yi Liu, Xuan Zhang, Ying Li +9
Video understanding is an important problem in computer vision. Currently, the well-studied task in this research is human action recognition, where the clips are manually trimmed…
Low-Resolution Action Recognition for Tiny Actions Challenge
Boyu Chen, Yu Qiao, Yali Wang
Tiny Actions Challenge focuses on understanding human activities in real-world surveillance. Basically, there are two main difficulties for activity recognition in this scenario. F…