3 citations · 10 across the 20 of their papers we have counts for
9 papers · 1 filter
Training-free Regional Prompting for Diffusion Transformers
Anthony Chen, Jianjin Xu, Wenzhao Zheng +5
Diffusion models have demonstrated excellent capabilities in text-to-image generation. Their semantic understanding (i.e., prompt following) ability has also been greatly improved…
DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes
Chensheng Peng, Chengwei Zhang, Yixiao Wang +6
We present DeSiRe-GS, a self-supervised gaussian splatting representation, enabling effective static-dynamic decomposition and high-fidelity surface reconstruction in complex drivi…
UniDrive: Towards Universal Driving Perception Across Camera Configurations
Ye Li, Wenzhao Zheng, Xiaonan Huang +1
Vision-centric autonomous driving has demonstrated excellent performance with economical sensors. As the fundamental step, 3D perception aims to infer 3D information from 2D images…
SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference
Yuan Zhang, Chun-Kai Fan, Junpeng Ma +8
In vision-language models (VLMs), visual tokens usually bear a significant amount of computational overhead despite sparsity of information in them when compared to text tokens. To…
FactorLLM: Factorizing Knowledge via Mixture of Experts for Large Language Models
Zhongyu Zhao, Menghang Dong, Rongyu Zhang +6
Recent research has demonstrated that Feed-Forward Networks (FFNs) in Large Language Models (LLMs) play a pivotal role in storing diverse linguistic and factual knowledge. Conventi…
Instruct Large Language Models to Drive like Humans
Ruijun Zhang, Xianda Guo, Wenzhao Zheng +3
Motion planning in complex scenarios is the core challenge in autonomous driving. Conventional methods apply predefined rules or learn from driving data to plan the future trajecto…