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Jingyu Lu

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NeurIPS Conference 2025 Conference Paper

ZEBRA: Towards Zero-Shot Cross-Subject Generalization for Universal Brain Visual Decoding

Haonan Wang
Jingyu Lu
Hongrui Li
Xiaomeng Li

Recent advances in neural decoding have enabled the reconstruction of visual experiences from brain activity, positioning fMRI-to-image reconstruction as a promising bridge between neuroscience and computer vision. However, current methods predominantly rely on subject-speciﬁc models or require subject-speciﬁc ﬁne-tuning, limiting their scalability and real-world applicability. In this work, we introduce ZEBRA, the ﬁrst zero-shot brain visual decoding framework that eliminates the need for subject-speciﬁc adaptation. Z EBRA is built on the key insight that fMRI representations can be decomposed into subject-related and semantic-related components. By leveraging adversarial training, our method explicitly disentangles these components to isolate subject-invariant, semantic-speciﬁc representations. This disentanglement allows ZEBRA to generalize to unseen subjects without any additional fMRI data or retraining. Extensive experiments show that ZEBRA signiﬁcantly outperforms zero-shot baselines and achieves performance comparable to fully ﬁnetuned models on several metrics. Our work represents a scalable and practical step toward universal neural decoding. Code and model weights are available at: https: //github. com/xmed-lab/ZEBRA.

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NeurIPS Conference 2024 Conference Paper

GTSinger: A Global Multi-Technique Singing Corpus with Realistic Music Scores for All Singing Tasks

Yu Zhang
Changhao Pan
Wenxiang Guo
Ruiqi Li
Zhiyuan Zhu
Jialei Wang
Wenhao Xu
Jingyu Lu

The scarcity of high-quality and multi-task singing datasets significantly hinders the development of diverse controllable and personalized singing tasks, as existing singing datasets suffer from low quality, limited diversity of languages and singers, absence of multi-technique information and realistic music scores, and poor task suitability. To tackle these problems, we present GTSinger, a large Global, multi-Technique, free-to-use, high-quality singing corpus with realistic music scores, designed for all singing tasks, along with its benchmarks. Particularly, (1) we collect 80. 59 hours of high-quality singing voices, forming the largest recorded singing dataset; (2) 20 professional singers across nine widely spoken languages offer diverse timbres and styles; (3) we provide controlled comparison and phoneme-level annotations of six commonly used singing techniques, helping technique modeling and control; (4) GTSinger offers realistic music scores, assisting real-world musical composition; (5) singing voices are accompanied by manual phoneme-to-audio alignments, global style labels, and 16. 16 hours of paired speech for various singing tasks. Moreover, to facilitate the use of GTSinger, we conduct four benchmark experiments: technique-controllable singing voice synthesis, technique recognition, style transfer, and speech-to-singing conversion.

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