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cs.CV2024
p-MoD: Building Mixture-of-Depths MLLMs via Progressive Ratio Decay
Jun Zhang, Desen Meng, Zhengming Zhang +3
Despite the remarkable performance of multimodal large language models (MLLMs) across diverse tasks, the substantial training and inference costs impede their advancement. In this…
cs.CV2024
VideoEval: Comprehensive Benchmark Suite for Low-Cost Evaluation of Video Foundation Model
Xinhao Li, Zhenpeng Huang, Jing Wang +2
With the growth of high-quality data and advancement in visual pre-training paradigms, Video Foundation Models (VFMs) have made significant progress recently, demonstrating their r…
cs.CV2024
Data-efficient Event Camera Pre-training via Disentangled Masked Modeling
Zhenpeng Huang, Chao Li, Hao Chen +3
In this paper, we present a new data-efficient voxel-based self-supervised learning method for event cameras. Our pre-training overcomes the limitations of previous methods, which…