3 papers
cs.LG2026
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…
cs.CV2026
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…
cs.CV2025
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…