90 citations · 215 across the 27 of their papers we have counts for
12 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…
Asymmetric Masked Distillation for Pre-Training Small Foundation Models
Zhiyu Zhao, Bingkun Huang, Sen Xing +3
Self-supervised foundation models have shown great potential in computer vision thanks to the pre-training paradigm of masked autoencoding. Scale is a primary factor influencing th…
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…
ZeroI2V: Zero-Cost Adaptation of Pre-trained Transformers from Image to Video
Xinhao Li, Yuhan Zhu, Limin Wang
Adapting image models to the video domain has emerged as an efficient paradigm for solving video recognition tasks. Due to the huge number of parameters and effective transferabili…
MGMAE: Motion Guided Masking for Video Masked Autoencoding
Bingkun Huang, Zhiyu Zhao, Guozhen Zhang +2
Masked autoencoding has shown excellent performance on self-supervised video representation learning. Temporal redundancy has led to a high masking ratio and customized masking str…
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…
VideoLLM: Modeling Video Sequence with Large Language Models
Guo Chen, Yin-Dong Zheng, Jiahao Wang +8
With the exponential growth of video data, there is an urgent need for automated technology to analyze and comprehend video content. However, existing video understanding models ar…
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…