3 papers
cs.CV2026
Growing a Multi-head Twig via Distillation and Reinforcement Learning to Accelerate Large Vision-Language Models
Zhenwei Shao, Mingyang Wang, Weijun Zhang +6
Large vision-language models (VLMs) have demonstrated remarkable capabilities in open-world multimodal understanding, yet their high computational overheads pose great challenges f…
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.CV2025
Sparse4DGS: 4D Gaussian Splatting for Sparse-Frame Dynamic Scene Reconstruction
Changyue Shi, Chuxiao Yang, Xinyuan Hu +6
Dynamic Gaussian Splatting approaches have achieved remarkable performance for 4D scene reconstruction. However, these approaches rely on dense-frame video sequences for photoreali…