Neural Style Transfer: A Review. - Yongcheng Jing; Yezhou Yang; Zunlei Feng; Jingwen Ye; Mingli Song (CoRR, 2017)

Finer-Net: Cascaded Human Parsing with Hierarchical Granularity. - Jingwen Ye; Zunlei Feng; Yongcheng Jing; Mingli Song (ICME, 2018)

Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More. - Jingwen Ye; Yixin Ji; Xinchao Wang; Kairi Ou; Dapeng Tao; Mingli Song (CVPR, 2019)

Amalgamating Filtered Knowledge: Learning Task-customized Student from Multi-task Teachers. - Jingwen Ye; Xinchao Wang; Yixin Ji; Kairi Ou; Mingli Song (IJCAI, 2019)

DEPARA: Deep Attribution Graph for Deep Knowledge Transferability. - Jie Song 011; Yixin Chen; Jingwen Ye; Xinchao Wang; Chengchao Shen; Feng Mao; Mingli Song (CVPR, 2020)

Data-Free Knowledge Amalgamation via Group-Stack Dual-GAN. - Jingwen Ye; Yixin Ji; Xinchao Wang; Xin Gao; Mingli Song (CVPR, 2020)

Edge-Sensitive Human Cutout With Hierarchical Granularity and Loopy Matting Guidance. - Jingwen Ye; Yongcheng Jing; Xinchao Wang; Kairi Ou; Dacheng Tao; Mingli Song (IEEE Trans. Image Process., 2020)

Neural Style Transfer: A Review. - Yongcheng Jing; Yezhou Yang; Zunlei Feng; Jingwen Ye; Yizhou Yu; Mingli Song (IEEE Trans. Vis. Comput. Graph., 2020)

Online Knowledge Distillation for Efficient Pose Estimation. - Zheng Li 028; Jingwen Ye; Mingli Song; Ying Huang 003; Zhigeng Pan (ICCV, 2021)

Towards End-to-End Embroidery Style Generation: A Paired Dataset and Benchmark. - Jingwen Ye; Yixin Ji; Jie Song 011; Zunlei Feng; Mingli Song (PRCV (4), 2021)

Safe Distillation Box. - Jingwen Ye; Yining Mao; Jie Song 011; Xinchao Wang; Cheng Jin 001; Mingli Song (AAAI, 2022)

Unleashing the Potential of Adaptation Models via Go-getting Domain Labels. - Xin Jin 014; Tianyu He; Xu Shen 001; Songhua Wu; Tongliang Liu; Jingwen Ye; Xinchao Wang; Jianqiang Huang; Zhibo Chen 001; Xian-Sheng Hua 001 (ECCV Workshops (8), 2022)

DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation. - Songhua Liu; Jingwen Ye; Sucheng Ren; Xinchao Wang (ECCV (16), 2022)

Factorizing Knowledge in Neural Networks. - Xingyi Yang; Jingwen Ye; Xinchao Wang (ECCV (34), 2022)

Learning with Recoverable Forgetting. - Jingwen Ye; Yifang Fu; Jie Song 011; Xingyi Yang; Songhua Liu; Xin Jin; Mingli Song; Xinchao Wang (ECCV (11), 2022)

Dataset Distillation via Factorization. - Songhua Liu; Kai Wang 036; Xingyi Yang; Jingwen Ye; Xinchao Wang (NeurIPS, 2022)

Deep Model Reassembly. - Xingyi Yang; Daquan Zhou; Songhua Liu; Jingwen Ye; Xinchao Wang (NeurIPS, 2022)

Flocking Birds of a Feather Together: Dual-step GAN Distillation via Realer-Fake Samples. - Jingwen Ye; Zunlei Feng; Xinchao Wang (VCIP, 2022)

A Survey of Neural Trees. - Haoling Li; Jie Song 011; Mengqi Xue; Haofei Zhang; Jingwen Ye; Lechao Cheng; Mingli Song (CoRR, 2022)

Data Cleaning and AutoML: Would an Optimizer Choose to Clean? - Felix Neutatz; Binger Chen; Yazan Alkhatib; Jingwen Ye; Ziawasch Abedjan (Datenbank-Spektrum, 2022)

Spot-Adaptive Knowledge Distillation. - Jie Song 011; Ying Chen; Jingwen Ye; Mingli Song (IEEE Trans. Image Process., 2022)

Slimmable Dataset Condensation. - Songhua Liu; Jingwen Ye; Runpeng Yu; Xinchao Wang (CVPR, 2023)

Partial Network Cloning. - Jingwen Ye; Songhua Liu; Xinchao Wang (CVPR, 2023)

Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization. - Kaixuan Chen 004; Shunyu Liu 001; Tongtian Zhu; Ji Qiao; Yun Su; Yingjie Tian 002; Tongya Zheng; Haofei Zhang; Zunlei Feng; Jingwen Ye; Mingli Song (KDD, 2023)

Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate. - Songhua Liu; Jingwen Ye; Xinchao Wang (CoRR, 2023)

Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization. - Kaixuan Chen 004; Shunyu Liu 001; Tongtian Zhu; Tongya Zheng; Haofei Zhang; Zunlei Feng; Jingwen Ye; Mingli Song (CoRR, 2023)

Mutual-Modality Adversarial Attack with Semantic Perturbation. - Jingwen Ye; Ruonan Yu; Songhua Liu; Xinchao Wang (AAAI, 2024)

Ungeneralizable Examples. - Jingwen Ye; Xinchao Wang (CVPR, 2024)

Distilled Datamodel with Reverse Gradient Matching. - Jingwen Ye; Ruonan Yu; Songhua Liu; Xinchao Wang (CVPR, 2024)

Teddy: Efficient Large-Scale Dataset Distillation via Taylor-Approximated Matching. - Ruonan Yu; Songhua Liu; Jingwen Ye; Xinchao Wang (ECCV (46), 2024)

StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization. - Songhua Liu; Xin Jin; Xingyi Yang; Jingwen Ye; Xinchao Wang (ICML, 2024)

Improving Adversarial Robustness via Feature Pattern Consistency Constraint. - Jiacong Hu; Jingwen Ye; Zunlei Feng; Jiazhen Yang; Shunyu Liu 001; Xiaotian Yu; Lingxiang Jia; Mingli Song (IJCAI, 2024)

Transformer Doctor: Diagnosing and Treating Vision Transformers. - Jiacong Hu; Hao Chen 041; Kejia Chen 007; Yang Gao 001; Jingwen Ye; Xingen Wang; Mingli Song; Zunlei Feng (NeurIPS, 2024)

Model LEGO: Creating Models Like Disassembling and Assembling Building Blocks. - Jiacong Hu; Jing Gao; Jingwen Ye; Yang Gao 001; Xingen Wang; Zunlei Feng; Mingli Song (NeurIPS, 2024)

Sperm Manipulation and Selection Platform Using Optoelectronic Tweezers. - Shunxiao Huang; Jiawei Zhao; Chunyuan Gan; Hongyi Xiong; Jingwen Ye; Hongyan Sun; Wenyan Niu; Ao Wang; Bo Chen; Zijin Zeng; Chan Li; Lin Feng (ROBIO, 2024)

Heavy Labels Out! Dataset Distillation with Label Space Lightening. - Ruonan Yu; Songhua Liu; Zigeng Chen; Jingwen Ye; Xinchao Wang (CoRR, 2024)

Adversarial Training: A Survey. - Mengnan Zhao; Lihe Zhang; Jingwen Ye; Huchuan Lu; Baocai Yin; Xinchao Wang (CoRR, 2024)

Reinforced Model Merging. - Jiaqi Han; Jingwen Ye; Shunyu Liu 001; Haofei Zhang; Jie Song 011; Zunlei Feng; Mingli Song (CoRR, 2025)

PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with Auto-Regressive Transformer. - Jingwen Ye; Yuze He; Yanning Zhou 003; Yiqin Zhu; Kaiwen Xiao; Yong-Jin Liu; Wei Yang 013; Xiao Han (CoRR, 2025)

From One to Many: Portable Model Construction With Independent Network Units. - Zhaocheng Lu; Haofei Zhang; Li Sun; Jiabin Xia; Jingwen Ye; Mingli Song (IEEE Internet Things J., 2025)