Yonglong Tian

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 53

Top Topics

Pedestrian Detection

Convolutional Neural Networks

Unsupervised Learning

Top Venues

Publications

Nate Gillman, Michael Freeman, Daksh Aggarwal, Chia-Hong Hsu, Calvin Luo, Yonglong Tian, Chen Sun
Self-Correcting Self-Consuming Loops for Generative Model Training. CoRR (2024)
Jiawei Yang, Katie Z. Luo, Jiefeng Li, Kilian Q. Weinberger, Yonglong Tian, Yue Wang
Denoising Vision Transformers. CoRR (2024)
Yonglong Tian, Lijie Fan, Kaifeng Chen, Dina Katabi, Dilip Krishnan, Phillip Isola
Learning Vision from Models Rivals Learning Vision from Data. CoRR (2023)
Lijie Fan, Kaifeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, Yonglong Tian
Scaling Laws of Synthetic Images for Model Training ... for Now. CoRR (2023)
Yonglong Tian, Lijie Fan, Phillip Isola, Huiwen Chang, Dilip Krishnan
StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners. CoRR (2023)
Tianhong Li, Lijie Fan, Yuan Yuan, Hao He, Yonglong Tian, Rogério Feris, Piotr Indyk, Dina Katabi
Addressing Feature Suppression in Unsupervised Visual Representations. WACV (2023)
Tianhong Li, Sangnie Bhardwaj, Yonglong Tian, Han Zhang, Jarred Barber, Dina Katabi, Guillaume Lajoie, Huiwen Chang, Dilip Krishnan
Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency. CoRR (2023)
Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models. CoRR (2023)
Lirui Wang, Kaiqing Zhang, Yunzhu Li, Yonglong Tian, Russ Tedrake
Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision? ICLR (2023)
Yonglong Tian, Lijie Fan, Phillip Isola, Huiwen Chang, Dilip Krishnan
StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners. NeurIPS (2023)
Tianyu Hua, Yonglong Tian, Sucheng Ren, Michalis Raptis, Hang Zhao, Leonid Sigal
Self-supervision through Random Segments with Autoregressive Coding (RandSAC). ICLR (2023)
Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi S. Jaakkola
Restart Sampling for Improving Generative Processes. NeurIPS (2023)
Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi S. Jaakkola
Restart Sampling for Improving Generative Processes. CoRR (2023)
Lijie Fan, Dilip Krishnan, Phillip Isola, Dina Katabi, Yonglong Tian
Improving CLIP Training with Language Rewrites. CoRR (2023)
Lijie Fan, Dilip Krishnan, Phillip Isola, Dina Katabi, Yonglong Tian
Improving CLIP Training with Language Rewrites. NeurIPS (2023)
Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models. ICML (2023)
Tianyu Hua, Yonglong Tian, Sucheng Ren, Hang Zhao, Leonid Sigal
Self-supervision through Random Segments with Autoregressive Coding (RandSAC). CoRR (2022)
Lu Mi, Hao Wang, Yonglong Tian, Hao He, Nir Shavit
Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate. AAAI (2022)
Lirui Wang, Kaiqing Zhang, Yunzhu Li, Yonglong Tian, Russ Tedrake
Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision? CoRR (2022)
Ali Jahanian, Xavier Puig, Yonglong Tian, Phillip Isola
Generative Models as a Data Source for Multiview Representation Learning. ICLR (2022)
Sucheng Ren, Zhengqi Gao, Tianyu Hua, Zihui Xue, Yonglong Tian, Shengfeng He, Hang Zhao
Co-advise: Cross Inductive Bias Distillation. CVPR (2022)
Boyang Deng, Sumith Kulal, Zhengyang Dong, Congyue Deng, Yonglong Tian, Jiajun Wu
Unsupervised Learning of Shape Programs with Repeatable Implicit Parts. NeurIPS (2022)
Yonglong Tian, Olivier J. Hénaff, Aäron van den Oord
Divide and Contrast: Self-supervised Learning from Uncurated Data. CoRR (2021)
Ali Jahanian, Xavier Puig, Yonglong Tian, Phillip Isola
Generative Models as a Data Source for Multiview Representation Learning. CoRR (2021)
Jindong Gu, Wei Liu, Yonglong Tian
Simple Distillation Baselines for Improving Small Self-supervised Models. CoRR (2021)
Sucheng Ren, Zhengqi Gao, Tianyu Hua, Zihui Xue, Yonglong Tian, Shengfeng He, Hang Zhao
Co-advise: Cross Inductive Bias Distillation. CoRR (2021)
Chen Sun, Arsha Nagrani, Yonglong Tian, Cordelia Schmid
Composable Augmentation Encoding for Video Representation Learning. ICCV (2021)
Chen Sun, Arsha Nagrani, Yonglong Tian, Cordelia Schmid
Composable Augmentation Encoding for Video Representation Learning. CoRR (2021)
Yonglong Tian, Olivier J. Hénaff, Aäron van den Oord
Divide and Contrast: Self-supervised Learning from Uncurated Data. ICCV (2021)
Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B. Tenenbaum, Phillip Isola
Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need? CoRR (2020)
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, Dilip Krishnan
Supervised Contrastive Learning. NeurIPS (2020)
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola
What Makes for Good Views for Contrastive Learning? NeurIPS (2020)
Yonglong Tian, Dilip Krishnan, Phillip Isola
Contrastive Multiview Coding. ECCV (11) (2020)
Yonglong Tian, Dilip Krishnan, Phillip Isola
Contrastive Representation Distillation. ICLR (2020)
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, Dilip Krishnan
Supervised Contrastive Learning. CoRR (2020)
Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B. Tenenbaum, Phillip Isola
Rethinking Few-Shot Image Classification: A Good Embedding is All You Need? ECCV (14) (2020)
Tianhong Li, Lijie Fan, Yuan Yuan, Hao He, Yonglong Tian, Dina Katabi
Information-Preserving Contrastive Learning for Self-Supervised Representations. CoRR (2020)
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola
What makes for good views for contrastive learning. CoRR (2020)
Hao He, Hao Wang, Guang-He Lee, Yonglong Tian
ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees. ICLR (Poster) (2019)
Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu
Learning to Infer and Execute 3D Shape Programs. CoRR (2019)
Yonglong Tian, Dilip Krishnan, Phillip Isola
Contrastive Representation Distillation. CoRR (2019)
Lu Mi, Hao Wang, Yonglong Tian, Nir Shavit
Training-Free Uncertainty Estimation for Neural Networks. CoRR (2019)
Yonglong Tian, Dilip Krishnan, Phillip Isola
Contrastive Multiview Coding. CoRR (2019)
Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu
Learning to Infer and Execute 3D Shape Programs. ICLR (Poster) (2019)
Mingmin Zhao, Yonglong Tian, Hang Zhao, Mohammad Abu Alsheikh, Tianhong Li, Rumen Hristov, Zachary Kabelac, Dina Katabi, Antonio Torralba
RF-based 3D skeletons. SIGCOMM (2018)
Mingmin Zhao, Tianhong Li, Mohammad Abu Alsheikh, Yonglong Tian, Hang Zhao, Antonio Torralba, Dina Katabi
Through-Wall Human Pose Estimation Using Radio Signals. CVPR (2018)
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka
Representation Learning on Graphs with Jumping Knowledge Networks. ICML (2018)
Yonglong Tian, Guang-He Lee, Hao He, Chen-Yu Hsu, Dina Katabi
RF-Based Fall Monitoring Using Convolutional Neural Networks. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2 (3) (2018)
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka
Representation Learning on Graphs with Jumping Knowledge Networks. CoRR (2018)
Wanli Ouyang, Xingyu Zeng, Xiaogang Wang, Shi Qiu, Ping Luo, Yonglong Tian, Hongsheng Li, Shuo Yang, Zhe Wang, Hongyang Li, Kun Wang, Junjie Yan, Chen Change Loy, Xiaoou Tang
DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks. IEEE Trans. Pattern Anal. Mach. Intell. 39 (7) (2017)
Wanli Ouyang, Xiaogang Wang, Xingyu Zeng, Shi Qiu, Ping Luo, Yonglong Tian, Hongsheng Li, Shuo Yang, Zhe Wang, Chen Change Loy, Xiaoou Tang
DeepID-Net: Deformable deep convolutional neural networks for object detection. CVPR (2015)
Yonglong Tian, Ping Luo, Xiaogang Wang, Xiaoou Tang
Deep Learning Strong Parts for Pedestrian Detection. ICCV (2015)
Yonglong Tian, Ping Luo, Xiaogang Wang, Xiaoou Tang
Pedestrian detection aided by deep learning semantic tasks. CVPR (2015)
Wanli Ouyang, Xiaogang Wang, Xingyu Zeng, Shi Qiu, Ping Luo, Yonglong Tian, Hongsheng Li, Shuo Yang, Zhe Wang, Chen Change Loy, Xiaoou Tang
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection. CoRR (2014)
Ping Luo, Yonglong Tian, Xiaogang Wang, Xiaoou Tang
Switchable Deep Network for Pedestrian Detection. CVPR (2014)
Yonglong Tian, Ping Luo, Xiaogang Wang, Xiaoou Tang
Pedestrian Detection aided by Deep Learning Semantic Tasks. CoRR (2014)
Wanli Ouyang, Ping Luo, Xingyu Zeng, Shi Qiu, Yonglong Tian, Hongsheng Li, Shuo Yang, Zhe Wang, Yuanjun Xiong, Chen Qian, Zhenyao Zhu, Ruohui Wang, Chen Change Loy, Xiaogang Wang, Xiaoou Tang
DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection. CoRR (2014)