From the 1 of 14 linked papers with an AI index.
14 papers
VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding
Xinhao Li, Yuhan Zhu, Xiangyu Zeng +24
VideoChat3 is a fully open, 4B-parameter video-centric multimodal large language model that combines an efficient Inflated 3D Vision Transformer and adaptive frame resolution with…
Imagine Before You Predict: Interleaved Latent Visual Reasoning for Video Event Prediction
Tianxiang Jiang, Linquan Wu, Sheng Xia +5
Video event prediction (VEP) requires models to infer unobserved future states from partial video evidence. Existing video MLLMs usually verbalize intermediate future reasoning in…
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…
EgoThinker: Unveiling Egocentric Reasoning with Spatio-Temporal CoT
Baoqi Pei, Yifei Huang, Jilan Xu +5
Egocentric video reasoning centers on an unobservable agent behind the camera who dynamically shapes the environment, requiring inference of hidden intentions and recognition of fi…
LvBench: A Benchmark for Long-form Video Understanding with Versatile Multi-modal Question Answering
Hongjie Zhang, Lu Dong, Yi Liu +4
Despite remarkable recent progress, existing long-form VideoQA datasets fall short of meeting the criteria for genuine long-form video understanding. This is primarily due to the u…
InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling
Yi Wang, Xinhao Li, Ziang Yan +13
This paper aims to improve the performance of video multimodal large language models (MLLM) via long and rich context (LRC) modeling. As a result, we develop a new version of Inter…