Tian, Yonglong

35 publications

ICLR 2025 Fluid: Scaling Autoregressive Text-to-Image Generative Models with Continuous Tokens Lijie Fan, Tianhong Li, Siyang Qin, Yuanzhen Li, Chen Sun, Michael Rubinstein, Deqing Sun, Kaiming He, Yonglong Tian

ICLR 2025 Personalized Representation from Personalized Generation Shobhita Sundaram, Julia Chae, Yonglong Tian, Sara Beery, Phillip Isola

ICLRW 2025 Self-Correcting Self-Consuming Loops for Generative Model Training Nate Gillman, Michael Freeman, Daksh Aggarwal, Chia-Hong Hsu, Calvin Luo, Yonglong Tian, Chen Sun

CVPR 2025 Vision-Language Models Do Not Understand Negation Kumail Alhamoud, Shaden Alshammari, Yonglong Tian, Guohao Li, Philip H.S. Torr, Yoon Kim, Marzyeh Ghassemi

NeurIPS 2024 Autoregressive Image Generation Without Vector Quantization Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, Kaiming He

ECCV 2024 Denoising Vision Transformers Jiawei Yang, Katie Z Luo, Jiefeng Li, Congyue Deng, Leonidas Guibas, Dilip Krishnan, Kilian Weinberger, Yonglong Tian, Yue Wang

CVPR 2024 Learning Vision from Models Rivals Learning Vision from Data Yonglong Tian, Lijie Fan, Kaifeng Chen, Dina Katabi, Dilip Krishnan, Phillip Isola

ICLR 2024 Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency Tianhong Li, Sangnie Bhardwaj, Yonglong Tian, Han Zhang, Jarred Barber, Dina Katabi, Guillaume Lajoie, Huiwen Chang, Dilip Krishnan

CVPR 2024 Scaling Laws of Synthetic Images for Model Training ... for Now Lijie Fan, Kaifeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, Yonglong Tian

ICML 2024 Self-Correcting Self-Consuming Loops for Generative Model Training Nate Gillman, Michael Freeman, Daksh Aggarwal, Chia-Hong Hsu, Calvin Luo, Yonglong Tian, Chen Sun

WACV 2023 Addressing Feature Suppression in Unsupervised Visual Representations Tianhong Li, Lijie Fan, Yuan Yuan, Hao He, Yonglong Tian, Rogerio Feris, Piotr Indyk, Dina Katabi

ICLR 2023 Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision? Lirui Wang, Kaiqing Zhang, Yunzhu Li, Yonglong Tian, Russ Tedrake

NeurIPS 2023 Improving CLIP Training with Language Rewrites Lijie Fan, Dilip Krishnan, Phillip Isola, Dina Katabi, Yonglong Tian

ICML 2023 PFGM++: Unlocking the Potential of Physics-Inspired Generative Models Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi Jaakkola

NeurIPS 2023 Restart Sampling for Improving Generative Processes Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi Jaakkola

ICLR 2023 Self-Supervision Through Random Segments with Autoregressive Coding (RandSAC) Tianyu Hua, Yonglong Tian, Sucheng Ren, Michalis Raptis, Hang Zhao, Leonid Sigal

NeurIPS 2023 StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners Yonglong Tian, Lijie Fan, Phillip Isola, Huiwen Chang, Dilip Krishnan

CVPR 2022 Co-Advise: Cross Inductive Bias Distillation Sucheng Ren, Zhengqi Gao, Tianyu Hua, Zihui Xue, Yonglong Tian, Shengfeng He, Hang Zhao

ICLR 2022 Generative Models as a Data Source for Multiview Representation Learning Ali Jahanian, Xavier Puig, Yonglong Tian, Phillip Isola

AAAI 2022 Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate Lu Mi, Hao Wang, Yonglong Tian, Hao He, Nir Shavit

NeurIPS 2022 Unsupervised Learning of Shape Programs with Repeatable Implicit Parts Boyang Deng, Sumith Kulal, Zhengyang Dong, Congyue Deng, Yonglong Tian, Jiajun Wu

ICCV 2021 Composable Augmentation Encoding for Video Representation Learning Chen Sun, Arsha Nagrani, Yonglong Tian, Cordelia Schmid

ICCV 2021 Divide and Contrast: Self-Supervised Learning from Uncurated Data Yonglong Tian, Olivier J. Hénaff, Aäron van den Oord

ECCV 2020 Contrastive Multiview Coding Yonglong Tian, Dilip Krishnan, Phillip Isola

ICLR 2020 Contrastive Representation Distillation Yonglong Tian, Dilip Krishnan, Phillip Isola

ECCV 2020 Rethinking Few-Shot Image Classification: A Good Embedding Is All You Need? Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B. Tenenbaum, Phillip Isola

NeurIPS 2020 Supervised Contrastive Learning Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, Dilip Krishnan

NeurIPS 2020 What Makes for Good Views for Contrastive Learning? Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola

ICLR 2019 Learning to Infer and Execute 3D Shape Programs Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu

ICLR 2019 ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees Hao He, Hao Wang, Guang-He Lee, Yonglong Tian

ICML 2018 Representation Learning on Graphs with Jumping Knowledge Networks Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka

ICCV 2015 Deep Learning Strong Parts for Pedestrian Detection Yonglong Tian, Ping Luo, Xiaogang Wang, Xiaoou Tang

CVPR 2015 DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection Wanli Ouyang, Xiaogang Wang, Xingyu Zeng, Shi Qiu, Ping Luo, Yonglong Tian, Hongsheng Li, Shuo Yang, Zhe Wang, Chen-Change Loy, Xiaoou Tang

CVPR 2015 Pedestrian Detection Aided by Deep Learning Semantic Tasks Yonglong Tian, Ping Luo, Xiaogang Wang, Xiaoou Tang

CVPR 2014 Switchable Deep Network for Pedestrian Detection Ping Luo, Yonglong Tian, Xiaogang Wang, Xiaoou Tang