1 citations · 1 across the 4 of their papers we have counts for
7 papers
Resolving Long-Tail Ambiguity in Unsupervised 3D Point Cloud Segmentation with Language Priors
Siqi Wei, Hongbin Xu, Feng Xiao +4
Existing approaches for unsupervised 3D point cloud segmentation predominantly rely on a purely visual similarity-based learning-by-clustering paradigm, which suffers from a fundam…
ToolTok: Tool Tokenization for Efficient and Generalizable GUI Agents
Xiaoce Wang, Guibin Zhang, Junzhe Li +3
Existing GUI agent models relying on coordinate-based one-step visual grounding struggle with generalizing to varying input resolutions and aspect ratios. Alternatives introduce co…
FedVLA: Federated Vision-Language-Action Learning with Dual Gating Mixture-of-Experts for Robotic Manipulation
Cui Miao, Tao Chang, Meihan Wu +4
Vision-language-action (VLA) models have significantly advanced robotic manipulation by enabling robots to interpret language instructions for task execution. However, training the…
Robust Brain Tumor Segmentation with Incomplete MRI Modalities Using Hölder Divergence and Mutual Information-Enhanced Knowledge Transfer
Runze Cheng, Xihang Qiu, Ming Li +3
Multimodal MRI provides critical complementary information for accurate brain tumor segmentation. However, conventional methods struggle when certain modalities are missing due to…
X-SGS: Safe and Generalizable Gaussian Splatting with X-dimensional Watermarks
Zihang Cheng, Wentao Bao, Huiping Zhuang +6
3D Gaussian Splatting (3DGS) has been widely used in 3D reconstruction and 3D generation. However, the rapid adoption of 3D Gaussian Splatting raises growing concerns about informa…
EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients
Meihan Wu, Tao Chang, Cui Miao +5
Federated learning research has recently shifted from Convolutional Neural Networks (CNNs) to Vision Transformers (ViTs) due to their superior capacity. ViTs training demands highe…
Uncertainty Quantification via Hölder Divergence for Multi-View Representation Learning
Yan Zhang, Ming Li, Chun Li +3
Evidence-based deep learning represents a burgeoning paradigm for uncertainty estimation, offering reliable predictions with negligible extra computational overheads. Existing meth…