Deep Reinforcement Learning for Surgical Gesture Segmentation and Classification. - Daochang Liu; Tingting Jiang 001 (MICCAI (4), 2018)

Completeness Modeling and Context Separation for Weakly Supervised Temporal Action Localization. - Daochang Liu; Tingting Jiang 001; Yizhou Wang 001 (CVPR, 2019)

Surgical Skill Assessment on In-Vivo Clinical Data via the Clearness of Operating Field. - Daochang Liu; Tingting Jiang 001; Yizhou Wang 001; Rulin Miao; Fei Shan; Ziyu Li (MICCAI (5), 2019)

Unsupervised Surgical Instrument Segmentation via Anchor Generation and Semantic Diffusion. - Daochang Liu; Yuhui Wei; Tingting Jiang 001; Yizhou Wang 001; Rulin Miao; Fei Shan; Ziyu Li (MICCAI (3), 2020)

Clearness of operating field: a surrogate for surgical skills on in vivo clinical data. - Daochang Liu; Tingting Jiang 001; Yizhou Wang 001; Rulin Miao; Fei Shan; Ziyu Li (Int. J. Comput. Assist. Radiol. Surg., 2020)

Towards Unified Surgical Skill Assessment. - Daochang Liu; Qiyue Li; Tingting Jiang 001; Yizhou Wang 001; Rulin Miao; Fei Shan; Ziyu Li (CVPR, 2021)

Contrastive Code-Comment Pre-training. - Xiaohuan Pei; Daochang Liu; Luo Qian; Chang Xu 002 (ICDM, 2022)

Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-Resolution. - Zhongwei Qiu; Huan Yang 005; Jianlong Fu; Daochang Liu; Chang Xu 002; Dongmei Fu (CoRR, 2022)

Private Image Generation with Dual-Purpose Auxiliary Classifier. - Chen Chen 074; Daochang Liu; Siqi Ma; Surya Nepal; Chang Xu 002 (CVPR, 2023)

Personalized Image Generation for Color Vision Deficiency Population. - Shuyi Jiang; Daochang Liu; Dingquan Li; Chang Xu 002 (ICCV, 2023)

Diffusion Action Segmentation. - Daochang Liu; Qiyue Li; Anh-Dung Dinh; Tingting Jiang 001; Mubarak Shah; Chang Xu 002 (ICCV, 2023)

PixelAsParam: A Gradient View on Diffusion Sampling with Guidance. - AnhDung Dinh; Daochang Liu; Chang Xu 002 (ICML, 2023)

Calibrating a Deep Neural Network with Its Predecessors. - Linwei Tao; Minjing Dong; Daochang Liu; Changming Sun; Chang Xu 002 (IJCAI, 2023)

Rethinking Conditional Diffusion Sampling with Progressive Guidance. - Anh-Dung Dinh; Daochang Liu; Chang Xu 002 (NeurIPS, 2023)

Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense. - Zunzhi You; Daochang Liu; Bohyung Han; Chang Xu 002 (NeurIPS, 2023)

Contrastive Sampling Chains in Diffusion Models. - Junyu Zhang; Daochang Liu; Shichao Zhang 001; Chang Xu 002 (NeurIPS, 2023)

Two-in-one Knowledge Distillation for Efficient Facial Forgery Detection. - Chuyang Zhou; Jiajun Huang; Daochang Liu; Chengbin Du; Siqi Ma; Surya Nepal; Chang Xu 002 (CoRR, 2023)

Efficient Transfer Learning in Diffusion Models via Adversarial Noise. - Xiyu Wang; Baijiong Lin; Daochang Liu; Chang Xu 002 (CoRR, 2023)

Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark. - Martin Wagner 001; Beat P. Müller-Stich; Anna Kisilenko; Duc Tran; Patrick Heger; Lars Mündermann; David M. Lubotsky; Benjamin Müller; Tornike Davitashvili; Manuela Capek; Annika Reinke; Carissa Reid; et al. (Medical Image Anal., 2023)

Learning Degradation-Robust Spatiotemporal Frequency-Transformer for Video Super-Resolution. - Zhongwei Qiu; Huan Yang 005; Jianlong Fu; Daochang Liu; Chang Xu 002; Dongmei Fu (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Towards Memorization-Free Diffusion Models. - Chen Chen 074; Daochang Liu; Chang Xu 002 (CVPR, 2024)

Residual Learning in Diffusion Models. - Junyu Zhang; Daochang Liu; Eunbyung Park; Shichao Zhang 001; Chang Xu 002 (CVPR, 2024)

Bridging Data Gaps in Diffusion Models with Adversarial Noise-Based Transfer Learning. - Xiyu Wang; Baijiong Lin; Daochang Liu; Ying-Cong Chen; Chang Xu 002 (ICML, 2024)

Boosting Diffusion Models with an Adaptive Momentum Sampler. - Xiyu Wang; Anh-Dung Dinh; Daochang Liu; Chang Xu 002 (IJCAI, 2024)

Collage Prompting: Budget-Friendly Visual Recognition with GPT-4V. - Siyu Xu; Yunke Wang; Daochang Liu; Chang Xu 002 (CoRR, 2024)

Surgical Triplet Recognition via Diffusion Model. - Daochang Liu; Axel Hu; Mubarak Shah; Chang Xu 002 (CoRR, 2024)

Compress Guidance in Conditional Diffusion Sampling. - Anh-Dung Dinh; Daochang Liu; Chang Xu 002 (CoRR, 2024)

Investigating Memorization in Video Diffusion Models. - Chen Chen 074; Enhuai Liu; Daochang Liu; Mubarak Shah; Chang Xu 002 (CoRR, 2024)

Enhancing Privacy-Utility Trade-offs to Mitigate Memorization in Diffusion Models. - Chen Chen 074; Daochang Liu; Mubarak Shah; Chang Xu 002 (CVPR, 2025)

Exploring Local Memorization in Diffusion Models via Bright Ending Attention. - Chen Chen 074; Daochang Liu; Mubarak Shah; Chang Xu 002 (ICLR, 2025)

Representative Guidance: Diffusion Model Sampling with Coherence. - Anh-Dung Dinh; Daochang Liu; Chang Xu 002 (ICLR, 2025)

Anti-Exposure Bias in Diffusion Models. - Junyu Zhang; Daochang Liu; Eunbyung Park; Shichao Zhang 001; Chang Xu 002 (ICLR, 2025)

CollagePrompt: A Benchmark for Budget-Friendly Visual Recognition with GPT-4V. - Siyu Xu; Yunke Wang; Daochang Liu; Bo Du 001; Chang Xu 002 (NAACL (Findings), 2025)

Workshop on Sustainable AI for the Future Web. - Chang Xu 002; Yunke Wang; Jianyuan Guo; Daochang Liu; Minjing Dong; Yasmeen George; Johan Barthelemy; Yan Liu 002; Ling Chen 006 (WWW (Companion Volume), 2025)

Generative Physical AI in Vision: A Survey. - Daochang Liu; Junyu Zhang; Anh-Dung Dinh; Eunbyung Park; Shichao Zhang 001; Chang Xu 002 (CoRR, 2025)

DiffAct++: Diffusion Action Segmentation. - Daochang Liu; Qiyue Li; Anh-Dung Dinh; Tingting Jiang 001; Mubarak Shah; Chang Xu 002 (IEEE Trans. Pattern Anal. Mach. Intell., 2025)