activity
20192024
most citedFLOP: Federated Learning on Medical Datasets using Partial Networks

65 citations · 281 across the 14 of their papers we have counts for

collaborators

14 papers

cs.SD2024

Music Era Recognition Using Supervised Contrastive Learning and Artist Information

Qiqi He, Xuchen Song, Weituo Hao +3

Does popular music from the 60s sound different than that of the 90s? Prior study has shown that there would exist some variations of patterns and regularities related to instrumen…

cs.SD2023

InstructME: An Instruction Guided Music Edit And Remix Framework with Latent Diffusion Models

Bing Han, Junyu Dai, Weituo Hao +6

Music editing primarily entails the modification of instrument tracks or remixing in the whole, which offers a novel reinterpretation of the original piece through a series of oper…

cs.CV2023★ 3 cited

Mitigating Test-Time Bias for Fair Image Retrieval

Fanjie Kong, Shuai Yuan, Weituo Hao +1

We address the challenge of generating fair and unbiased image retrieval results given neutral textual queries (with no explicit gender or race connotations), while maintaining the…

cs.SD2022★ 1 cited

ALCAP: Alignment-Augmented Music Captioner

Zihao He, Weituo Hao, Wei-Tsung Lu +3

Music captioning has gained significant attention in the wake of the rising prominence of streaming media platforms. Traditional approaches often prioritize either the audio or lyr…

cs.CV2021★ 7 cited

Towards Fair Federated Learning with Zero-Shot Data Augmentation

Weituo Hao, Mostafa El-Khamy, Jungwon Lee +4

Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models while having no access to the…

eess.AS2021★ 27 cited

Improving Zero-shot Voice Style Transfer via Disentangled Representation Learning

Siyang Yuan, Pengyu Cheng, Ruiyi Zhang +3

Voice style transfer, also called voice conversion, seeks to modify one speaker's voice to generate speech as if it came from another (target) speaker. Previous works have made pro…