3 citations · 11 across the 13 of their papers we have counts for
21 papers · 1 filter
Mining Attribute Subspaces for Efficient Fine-tuning of 3D Foundation Models
Yu Jiang, Hanwen Jiang, Ahmed Abdelkader +4
With the emergence of 3D foundation models, there is growing interest in fine-tuning them for downstream tasks, where LoRA is the dominant fine-tuning paradigm. As 3D datasets exhi…
Can Test-Time Scaling Improve World Foundation Model?
Wenyan Cong, Hanqing Zhu, Peihao Wang +7
World foundation models, which simulate the physical world by predicting future states from current observations and inputs, have become central to many applications in physical in…
VideoLifter: Lifting Videos to 3D with Fast Hierarchical Stereo Alignment
Wenyan Cong, Hanqing Zhu, Kevin Wang +6
Efficiently reconstructing 3D scenes from monocular video remains a core challenge in computer vision, vital for applications in virtual reality, robotics, and scene understanding.…
Chasing Better Deep Image Priors between Over- and Under-parameterization
Qiming Wu, Xiaohan Chen, Yifan Jiang +1
Deep Neural Networks (DNNs) are well-known to act as over-parameterized deep image priors (DIP) that regularize various image inverse problems. Meanwhile, researchers also proposed…
Large Spatial Model: End-to-end Unposed Images to Semantic 3D
Zhiwen Fan, Jian Zhang, Wenyan Cong +10
Reconstructing and understanding 3D structures from a limited number of images is a well-established problem in computer vision. Traditional methods usually break this task into mu…
Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses
Zhiwen Fan, Pu Wang, Yang Zhao +5
The increasing rate of road accidents worldwide results not only in significant loss of life but also imposes billions financial burdens on societies. Current research in traffic c…