Switchable Deep Network for Pedestrian Detection. - Ping Luo 002; Yonglong Tian; Xiaogang Wang 001; Xiaoou Tang (CVPR, 2014)

DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection. - Wanli Ouyang; Ping Luo 002; Xingyu Zeng; Shi Qiu; Yonglong Tian; Hongsheng Li 001; Shuo Yang 003; Zhe Wang 006; Yuanjun Xiong; Chen Qian 006; Zhenyao Zhu; Ruohui Wang; et al. (CoRR, 2014)

DeepID-Net: Deformable deep convolutional neural networks for object detection. - Wanli Ouyang; Xiaogang Wang 001; Xingyu Zeng; Shi Qiu; Ping Luo 002; Yonglong Tian; Hongsheng Li 001; Shuo Yang 003; Zhe Wang 006; Chen Change Loy; Xiaoou Tang (CVPR, 2015)

Pedestrian detection aided by deep learning semantic tasks. - Yonglong Tian; Ping Luo 002; Xiaogang Wang 001; Xiaoou Tang (CVPR, 2015)

Deep Learning Strong Parts for Pedestrian Detection. - Yonglong Tian; Ping Luo 002; Xiaogang Wang 001; Xiaoou Tang (ICCV, 2015)

DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks. - Wanli Ouyang; Xingyu Zeng; Xiaogang Wang 001; Shi Qiu; Ping Luo 002; Yonglong Tian; Hongsheng Li 001; Shuo Yang 003; Zhe Wang 006; Hongyang Li 001; Kun Wang; Junjie Yan; et al. (IEEE Trans. Pattern Anal. Mach. Intell., 2017)

Through-Wall Human Pose Estimation Using Radio Signals. - Mingmin Zhao; Tianhong Li; Mohammad Abu Alsheikh; Yonglong Tian; Hang Zhao; Antonio Torralba 001; Dina Katabi (CVPR, 2018)

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

RF-based 3D skeletons. - Mingmin Zhao; Yonglong Tian; Hang Zhao; Mohammad Abu Alsheikh; Tianhong Li; Rumen Hristov; Zachary Kabelac; Dina Katabi; Antonio Torralba 001 (SIGCOMM, 2018)

RF-Based Fall Monitoring Using Convolutional Neural Networks. - Yonglong Tian; Guang-He Lee; Hao He 011; Chen-Yu Hsu 001; Dina Katabi (Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2018)

ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees. - Hao He 011; Hao Wang 014; Guang-He Lee; Yonglong Tian (ICLR (Poster), 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 001 (ICLR (Poster), 2019)

Training-Free Uncertainty Estimation for Neural Networks. - Lu Mi; Hao Wang 014; Yonglong Tian; Nir Shavit (CoRR, 2019)

Contrastive Multiview Coding. - Yonglong Tian; Dilip Krishnan; Phillip Isola (ECCV (11), 2020)

Rethinking Few-Shot Image Classification: A Good Embedding is All You Need? - Yonglong Tian; Yue Wang 041; Dilip Krishnan; Joshua B. Tenenbaum; Phillip Isola (ECCV (14), 2020)

Contrastive Representation Distillation. - Yonglong Tian; Dilip Krishnan; Phillip Isola (ICLR, 2020)

Supervised Contrastive Learning. - Prannay Khosla; Piotr Teterwak; Chen Wang 050; Aaron Sarna; Yonglong Tian; Phillip Isola; Aaron Maschinot; Ce Liu 001; Dilip Krishnan (NeurIPS, 2020)

What Makes for Good Views for Contrastive Learning? - Yonglong Tian; Chen Sun 002; Ben Poole; Dilip Krishnan; Cordelia Schmid; Phillip Isola (NeurIPS, 2020)

Information-Preserving Contrastive Learning for Self-Supervised Representations. - Tianhong Li; Lijie Fan; Yuan Yuan 002; Hao He 011; Yonglong Tian; Dina Katabi (CoRR, 2020)

Composable Augmentation Encoding for Video Representation Learning. - Chen Sun 002; 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 (ICCV, 2021)

Simple Distillation Baselines for Improving Small Self-supervised Models. - Jindong Gu; Wei Liu 005; Yonglong Tian (CoRR, 2021)

Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate. - Lu Mi; Hao Wang 014; Yonglong Tian; Hao He 011; Nir Shavit (AAAI, 2022)

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

Generative Models as a Data Source for Multiview Representation Learning. - Ali Jahanian 002; Xavier Puig; Yonglong Tian; Phillip Isola (ICLR, 2022)

Unsupervised Learning of Shape Programs with Repeatable Implicit Parts. - Boyang Deng; Sumith Kulal; Zhengyang Dong; Congyue Deng; Yonglong Tian; Jiajun Wu 001 (NeurIPS, 2022)

Self-supervision through Random Segments with Autoregressive Coding (RandSAC). - Tianyu Hua; Yonglong Tian; Sucheng Ren; Hang Zhao; Leonid Sigal (CoRR, 2022)

Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision? - Lirui Wang; Kaiqing Zhang; Yunzhu Li; Yonglong Tian; Russ Tedrake (CoRR, 2022)

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

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

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

Improving CLIP Training with Language Rewrites. - Lijie Fan; Dilip Krishnan; Phillip Isola; Dina Katabi; Yonglong Tian (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 (NeurIPS, 2023)

Restart Sampling for Improving Generative Processes. - Yilun Xu; Mingyang Deng; Xiang Cheng; Yonglong Tian; Ziming Liu; Tommi S. Jaakkola (NeurIPS, 2023)

Addressing Feature Suppression in Unsupervised Visual Representations. - Tianhong Li; Lijie Fan; Yuan Yuan 002; Hao He 011; Yonglong Tian; Rogério Feris; Piotr Indyk; Dina Katabi (WACV, 2023)

Towards General-purpose Vision via Multiview Contrastive Learning - Yonglong Tian (2023)

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

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

Denoising Vision Transformers. - Jiawei Yang 002; Katie Z. Luo; Jiefeng Li; Congyue Deng; Leonidas J. Guibas; Dilip Krishnan; Kilian Q. Weinberger; Yonglong Tian; Yue Wang 041 (ECCV (85), 2024)

Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency. - Tianhong Li; Sangnie Bhardwaj; Yonglong Tian; Han Zhang 010; Jarred Barber; Dina Katabi; Guillaume Lajoie; Huiwen Chang; Dilip Krishnan (ICLR, 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 002 (ICML, 2024)

Autoregressive Image Generation without Vector Quantization. - Tianhong Li; Yonglong Tian; He Li; Mingyang Deng; Kaiming He (NeurIPS, 2024)

Denoising Vision Transformers. - Jiawei Yang 002; Katie Z. Luo; Jiefeng Li; Kilian Q. Weinberger; Yonglong Tian; Yue Wang 041 (CoRR, 2024)

Vision-Language Models Do Not Understand Negation. - Kumail Alhamoud; Shaden Alshammari; Yonglong Tian; Guohao Li 013; Philip H. S. Torr; Yoon Kim; Marzyeh Ghassemi (CVPR, 2025)

Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens. - Lijie Fan; Tianhong Li; Siyang Qin; Yuanzhen Li; Chen Sun 002; 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 (ICLR, 2025)

Vision-Language Models Do Not Understand Negation. - Kumail Alhamoud; Shaden Alshammari; Yonglong Tian; Guohao Li 001; Philip Torr 001; Yoon Kim; Marzyeh Ghassemi (CoRR, 2025)

Latent Denoising Makes Good Visual Tokenizers. - Jiawei Yang; Tianhong Li; Lijie Fan; Yonglong Tian; Yue Wang (CoRR, 2025)