1 citations · 1 across the 5 of their papers we have counts for
6 papers
MedGSSR: Generalizable Medical Image Super-Resolution 3D Reconstruction via Hierarchical Feed-forward Gaussian Splatting
Chengkai Wang, Luoyu Hong, Yiting Zhao +7
High-resolution volumetric medical imaging is critical for clinical diagnosis, yet acquisition is often limited by scanner hardware, scan time, and for CT, radiation dose. Medical…
FedAFD: Multimodal Federated Learning via Adversarial Fusion and Distillation
Min Tan, Junchao Ma, Yinfu Feng +6
Multimodal Federated Learning (MFL) enables clients with heterogeneous data modalities to collaboratively train models without sharing raw data, offering a privacy-preserving frame…
SR3R: Rethinking Super-Resolution 3D Reconstruction With Feed-Forward Gaussian Splatting
Xiang Feng, Xiangbo Wang, Tieshi Zhong +7
3D super-resolution (3DSR) aims to reconstruct high-resolution (HR) 3D scenes from low-resolution (LR) multi-view images. Existing methods rely on dense LR inputs and per-scene opt…
IE-SRGS: An Internal-External Knowledge Fusion Framework for High-Fidelity 3D Gaussian Splatting Super-Resolution
Xiang Feng, Tieshi Zhong, Shuo Chang +7
Reconstructing high-resolution (HR) 3D Gaussian Splatting (3DGS) models from low-resolution (LR) inputs remains challenging due to the lack of fine-grained textures and geometry. E…
Imp: Highly Capable Large Multimodal Models for Mobile Devices
Zhenwei Shao, Zhou Yu, Jun Yu +5
By harnessing the capabilities of large language models (LLMs), recent large multimodal models (LMMs) have shown remarkable versatility in open-world multimodal understanding. Neve…
SRGS: Super-Resolution 3D Gaussian Splatting
Xiang Feng, Yongbo He, Linxi Chen +8
Low-resolution (LR) multi-view capture limits the fidelity of 3D Gaussian Splatting (3DGS). 3DGS super-resolution (SR) is therefore important, yet challenging because it must recov…