2 citations · 6 across the 12 of their papers we have counts for
12 papers
CDCP: Conditional Diffusion Model with Contextual Prompts for Multi-task Offline Safe Reinforcement Learning
Jiayi Guan, Tianle Zhang, Li Shen +8
Multi-task offline safe reinforcement learning (RL) promises to learn a shared optimal safe policy from offline data across multiple tasks. This paradigm provides an effective mean…
HGL: Hierarchical Geometry Learning for Test-time Adaptation in 3D Point Cloud Segmentation
Tianpei Zou, Sanqing Qu, Zhijun Li +4
3D point cloud segmentation has received significant interest for its growing applications. However, the generalization ability of models suffers in dynamic scenarios due to the di…
GeoNLF: Geometry guided Pose-Free Neural LiDAR Fields
Weiyi Xue, Zehan Zheng, Fan Lu +3
Although recent efforts have extended Neural Radiance Fields (NeRF) into LiDAR point cloud synthesis, the majority of existing works exhibit a strong dependence on precomputed pose…
RCDN: Towards Robust Camera-Insensitivity Collaborative Perception via Dynamic Feature-based 3D Neural Modeling
Tianhang Wang, Fan Lu, Zehan Zheng +3
Collaborative perception is dedicated to tackling the constraints of single-agent perception, such as occlusions, based on the multiple agents' multi-view sensor inputs. However, m…
Urban Architect: Steerable 3D Urban Scene Generation with Layout Prior
Fan Lu, Kwan-Yee Lin, Yan Xu +3
Text-to-3D generation has achieved remarkable success via large-scale text-to-image diffusion models. Nevertheless, there is no paradigm for scaling up the methodology to urban sca…
LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis
Zehan Zheng, Fan Lu, Weiyi Xue +2
Although neural radiance fields (NeRFs) have achieved triumphs in image novel view synthesis (NVS), LiDAR NVS remains largely unexplored. Previous LiDAR NVS methods employ a simple…
MAP: MAsk-Pruning for Source-Free Model Intellectual Property Protection
Boyang Peng, Sanqing Qu, Yong Wu +5
Deep learning has achieved remarkable progress in various applications, heightening the importance of safeguarding the intellectual property (IP) of well-trained models. It entails…
LEAD: Learning Decomposition for Source-free Universal Domain Adaptation
Sanqing Qu, Tianpei Zou, Lianghua He +4
Universal Domain Adaptation (UniDA) targets knowledge transfer in the presence of both covariate and label shifts. Recently, Source-free Universal Domain Adaptation (SF-UniDA) has…
GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning
Sanqing Qu, Tianpei Zou, Florian Röhrbein +4
Deep neural networks often exhibit sub-optimal performance under covariate and category shifts. Source-Free Domain Adaptation (SFDA) presents a promising solution to this dilemma,…
Pensieve: Retrospect-then-Compare Mitigates Visual Hallucination
Dingchen Yang, Bowen Cao, Guang Chen +1
Multi-modal Large Language Models (MLLMs) demonstrate remarkable success across various vision-language tasks. However, they suffer from visual hallucination, where the generated r…
Spreeze: High-Throughput Parallel Reinforcement Learning Framework
Jing Hou, Guang Chen, Ruiqi Zhang +3
The promotion of large-scale applications of reinforcement learning (RL) requires efficient training computation. While existing parallel RL frameworks encompass a variety of RL al…
Urban Radiance Field Representation with Deformable Neural Mesh Primitives
Fan Lu, Yan Xu, Guang Chen +3
Neural Radiance Fields (NeRFs) have achieved great success in the past few years. However, most current methods still require intensive resources due to ray marching-based renderin…