Yonglong Tian
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
29
Venues
8
Active years
2014–2025
Best venue rank
A*
Where they publish
Papers
29 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | CVPR | Vision-Language Models Do Not Understand Negation. | Kumail Alhamoud, Shaden Alshammari, Yonglong Tian, Guohao Li, Philip H. S. Torr, Yoon Kim, Marzyeh Ghassemi |
| 2025 | ICLR | 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 |
| 2025 | ICLR | Personalized Representation from Personalized Generation. | Shobhita Sundaram, Julia Chae, Yonglong Tian, Sara Beery, Phillip Isola |
| 2024 | CVPR | Scaling Laws of Synthetic Images for Model Training ... for Now. | Lijie Fan, Kaifeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, Yonglong Tian |
| 2024 | CVPR | Learning Vision from Models Rivals Learning Vision from Data. | Yonglong Tian, Lijie Fan, Kaifeng Chen, Dina Katabi, Dilip Krishnan, Phillip Isola |
| 2024 | ECCV | Denoising Vision Transformers. | Jiawei Yang, Katie Z. Luo, Jiefeng Li, Congyue Deng, Leonidas J. Guibas, Dilip Krishnan, Kilian Q. Weinberger, Yonglong Tian, Yue Wang |
| 2024 | ICLR | 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 |
| 2024 | ICML | Self-Correcting Self-Consuming Loops for Generative Model Training. | Nate Gillman, Michael Freeman, Daksh Aggarwal, Chia-Hong Hsu, Calvin Luo, Yonglong Tian, Chen Sun |
| 2023 | ICLR | Self-supervision through Random Segments with Autoregressive Coding (RandSAC). | Tianyu Hua, Yonglong Tian, Sucheng Ren, Michalis Raptis, Hang Zhao, Leonid Sigal |
| 2023 | ICLR | Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision? | Lirui Wang, Kaiqing Zhang, Yunzhu Li, Yonglong Tian, Russ Tedrake |
| 2023 | ICML | PFGM++: Unlocking the Potential of Physics-Inspired Generative Models. | Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola |
| 2023 | WACV | Addressing Feature Suppression in Unsupervised Visual Representations. | Tianhong Li, Lijie Fan, Yuan Yuan, Hao He, Yonglong Tian, Rogrio Feris, Piotr Indyk, Dina Katabi |
| 2022 | AAAI | Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate. | Lu Mi, Hao Wang, Yonglong Tian, Hao He, Nir Shavit |
| 2022 | CVPR | Co-advise: Cross Inductive Bias Distillation. | Sucheng Ren, Zhengqi Gao, Tianyu Hua, Zihui Xue, Yonglong Tian, Shengfeng He, Hang Zhao |
| 2022 | ICLR | Generative Models as a Data Source for Multiview Representation Learning. | Ali Jahanian, Xavier Puig, Yonglong Tian, Phillip Isola |
| 2021 | ICCV | Composable Augmentation Encoding for Video Representation Learning. | Chen Sun, Arsha Nagrani, Yonglong Tian, Cordelia Schmid |
| 2021 | ICCV | Divide and Contrast: Self-supervised Learning from Uncurated Data. | Yonglong Tian, Olivier J. Hnaff, Aron van den Oord |
| 2020 | ECCV | Contrastive Multiview Coding. | Yonglong Tian, Dilip Krishnan, Phillip Isola |
| 2020 | ECCV | Rethinking Few-Shot Image Classification: A Good Embedding is All You Need? | Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B. Tenenbaum, Phillip Isola |
| 2020 | ICLR | Contrastive Representation Distillation. | Yonglong Tian, Dilip Krishnan, Phillip Isola |
| 2019 | ICLR | ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees. | Hao He, Hao Wang, Guang-He Lee, Yonglong Tian |
| 2019 | ICLR | Learning to Infer and Execute 3D Shape Programs. | Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu |
| 2018 | CVPR | Through-Wall Human Pose Estimation Using Radio Signals. | Mingmin Zhao, Tianhong Li, Mohammad Abu Alsheikh, Yonglong Tian, Hang Zhao, Antonio Torralba, Dina Katabi |
| 2018 | ICML | Representation Learning on Graphs with Jumping Knowledge Networks. | Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka |
| 2018 | SIGCOMM | RF-based 3D skeletons. | Mingmin Zhao, Yonglong Tian, Hang Zhao, Mohammad Abu Alsheikh, Tianhong Li, Rumen Hristov, Zachary Kabelac, Dina Katabi, Antonio Torralba |
| 2015 | CVPR | 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 |
| 2015 | CVPR | Pedestrian detection aided by deep learning semantic tasks. | Yonglong Tian, Ping Luo, Xiaogang Wang, Xiaoou Tang |
| 2015 | ICCV | Deep Learning Strong Parts for Pedestrian Detection. | Yonglong Tian, Ping Luo, Xiaogang Wang, Xiaoou Tang |
| 2014 | CVPR | Switchable Deep Network for Pedestrian Detection. | Ping Luo, Yonglong Tian, Xiaogang Wang, Xiaoou Tang |