93 citations · 427 across the 35 of their papers we have counts for
9 papers · 1 filter
VBench: Comprehensive Benchmark Suite for Video Generative Models
Ziqi Huang, Yinan He, Jiashuo Yu +13
Video generation has witnessed significant advancements, yet evaluating these models remains a challenge. A comprehensive evaluation benchmark for video generation is indispensable…
MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
Kunchang Li, Yali Wang, Yinan He +9
With the rapid development of Multi-modal Large Language Models (MLLMs), a number of diagnostic benchmarks have recently emerged to evaluate the comprehension capabilities of these…
Harvest Video Foundation Models via Efficient Post-Pretraining
Yizhuo Li, Kunchang Li, Yinan He +5
Building video-language foundation models is costly and difficult due to the redundant nature of video data and the lack of high-quality video-language datasets. In this paper, we…
LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models
Yaohui Wang, Xinyuan Chen, Xin Ma +17
This work aims to learn a high-quality text-to-video (T2V) generative model by leveraging a pre-trained text-to-image (T2I) model as a basis. It is a highly desirable yet challengi…
InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation
Yi Wang, Yinan He, Yizhuo Li +13
This paper introduces InternVid, a large-scale video-centric multimodal dataset that enables learning powerful and transferable video-text representations for multimodal understand…
InternGPT: Solving Vision-Centric Tasks by Interacting with ChatGPT Beyond Language
Zhaoyang Liu, Yinan He, Wenhai Wang +17
We present an interactive visual framework named InternGPT, or iGPT for short. The framework integrates chatbots that have planning and reasoning capabilities, such as ChatGPT, wit…
VideoChat: Chat-Centric Video Understanding
KunChang Li, Yinan He, Yi Wang +6
In this paper, we initiate an attempt of developing an end-to-end chat-centric video understanding system, coined as VideoChat. It integrates video foundation models and large lang…
VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking
Limin Wang, Bingkun Huang, Zhiyu Zhao +5
Scale is the primary factor for building a powerful foundation model that could well generalize to a variety of downstream tasks. However, it is still challenging to train video fo…
Unmasked Teacher: Towards Training-Efficient Video Foundation Models
Kunchang Li, Yali Wang, Yizhuo Li +4
Video Foundation Models (VFMs) have received limited exploration due to high computational costs and data scarcity. Previous VFMs rely on Image Foundation Models (IFMs), which face…