Tianran Chen — accepted AI-conference papers

2026

S2FT: Parameter-Efficient Fine-Tuning in Sparse Spectrum Domain

Baoquan Zhang, Zhehao Yu, Lisai Zhang, Kenghong Lin, Tianran Chen, Yuxi Sun +2

CVPR 2026

Parameter Efficient Fine-Tuning (PEFT) is a key technique for adapting a large pretrained model to downstream tasks by fine-tuning only a small number of parameters. Recent methods based on Fourier transforms have further reduced the fine-tuned parameters scale by only fine-tuning a few spectral coe

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2025

Sensitivity-Aware Efficient Fine-Tuning via Compact Dynamic-Rank Adaptation

Tianran Chen, Jiarui Chen, Baoquan Zhang, Zhehao Yu, Shidong Chen, Rui Ye +2

CVPR 2025poster

Parameter-Efficient Fine-Tuning (PEFT) is a fundamental research problem in computer vision, which aims to tune a few of parameters for efficient storage and adaptation of pre-trained vision models. Recently, sensitivity-aware parameter efficient fine-tuning method (SPT) addresses this problem by id…

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