most citedUnimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation

5 citations · 11 across the 3 of their papers we have counts for

collaborators

3 papers

cs.AI2024★ 2 cited

LLMs Meet Multimodal Generation and Editing: A Survey

Yingqing He, Zhaoyang Liu, Jingye Chen +13

With the recent advancement in large language models (LLMs), there is a growing interest in combining LLMs with multimodal learning. Previous surveys of multimodal large language m…

cs.SD2024★ 4 cited

ChatMusician: Understanding and Generating Music Intrinsically with LLM

Ruibin Yuan, Hanfeng Lin, Yi Wang +32

While Large Language Models (LLMs) demonstrate impressive capabilities in text generation, we find that their ability has yet to be generalized to music, humanity's creative langua…

cs.LG2023★ 5 cited

Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation

Rongyu Zhang, Xiaowei Chi, Guiliang Liu +3

Multimodal learning has seen great success mining data features from multiple modalities with remarkable model performance improvement. Meanwhile, federated learning (FL) addresses…