Yonglong Tian - RankMe

72 papers A* 30A 1Journal 35Unranked 5

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens. ICLR Lijie Fan, Tianhong Li, Siyang Qin, Yuanzhen Li, Chen Sun, Michael Rubinstein, Deqing Sun, Kaiming He, Yonglong Tian
2025	J	`jnl`	Latent Denoising Makes Good Visual Tokenizers. CoRR Jiawei Yang, Tianhong Li, Lijie Fan, Yonglong Tian, Yue Wang
2025	A*	`conf`	Personalized Representation from Personalized Generation. ICLR Shobhita Sundaram, Julia Chae, Yonglong Tian, Sara Beery, Phillip Isola
2025	A*	`conf`	Vision-Language Models Do Not Understand Negation. CVPR Kumail Alhamoud, Shaden Alshammari, Yonglong Tian, Guohao Li, Philip H. S. Torr, Yoon Kim, Marzyeh Ghassemi
2025	J	`jnl`	Vision-Language Models Do Not Understand Negation. CoRR Kumail Alhamoud, Shaden Alshammari, Yonglong Tian, Guohao Li, Philip Torr, Yoon Kim, Marzyeh Ghassemi
2024	A*	`conf`	Autoregressive Image Generation without Vector Quantization. NeurIPS Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, Kaiming He
2024	J	`jnl`	Autoregressive Image Generation without Vector Quantization. CoRR Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, Kaiming He
2024	—	`conf`	Denoising Vision Transformers. ECCV (85) Jiawei Yang, Katie Z. Luo, Jiefeng Li, Congyue Deng, Leonidas J. Guibas, Dilip Krishnan, Kilian Q. Weinberger, Yonglong Tian, Yue Wang
2024	J	`jnl`	Denoising Vision Transformers. CoRR Jiawei Yang, Katie Z. Luo, Jiefeng Li, Kilian Q. Weinberger, Yonglong Tian, Yue Wang
2024	J	`jnl`	Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens. CoRR Lijie Fan, Tianhong Li, Siyang Qin, Yuanzhen Li, Chen Sun, Michael Rubinstein, Deqing Sun, Kaiming He, Yonglong Tian
2024	A*	`conf`	Learning Vision from Models Rivals Learning Vision from Data. CVPR Yonglong Tian, Lijie Fan, Kaifeng Chen, Dina Katabi, Dilip Krishnan, Phillip Isola
2024	A*	`conf`	Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency. ICLR Tianhong Li, Sangnie Bhardwaj, Yonglong Tian, Han Zhang, Jarred Barber, Dina Katabi, Guillaume Lajoie, Huiwen Chang, Dilip Krishnan
2024	J	`jnl`	Personalized Representation from Personalized Generation. CoRR Shobhita Sundaram, Julia Chae, Yonglong Tian, Sara Beery, Phillip Isola
2024	A*	`conf`	Scaling Laws of Synthetic Images for Model Training ... for Now. CVPR Lijie Fan, Kaifeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, Yonglong Tian
2024	A*	`conf`	Self-Correcting Self-Consuming Loops for Generative Model Training. ICML Nate Gillman, Michael Freeman, Daksh Aggarwal, Chia-Hong Hsu, Calvin Luo, Yonglong Tian, Chen Sun
2024	J	`jnl`	Self-Correcting Self-Consuming Loops for Generative Model Training. CoRR Nate Gillman, Michael Freeman, Daksh Aggarwal, Chia-Hong Hsu, Calvin Luo, Yonglong Tian, Chen Sun
2023	A	`conf`	Addressing Feature Suppression in Unsupervised Visual Representations. WACV Tianhong Li, Lijie Fan, Yuan Yuan, Hao He, Yonglong Tian, Rogério Feris, Piotr Indyk, Dina Katabi
2023	A*	`conf`	Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision? ICLR Lirui Wang, Kaiqing Zhang, Yunzhu Li, Yonglong Tian, Russ Tedrake
2023	A*	`conf`	Improving CLIP Training with Language Rewrites. NeurIPS Lijie Fan, Dilip Krishnan, Phillip Isola, Dina Katabi, Yonglong Tian
2023	J	`jnl`	Improving CLIP Training with Language Rewrites. CoRR Lijie Fan, Dilip Krishnan, Phillip Isola, Dina Katabi, Yonglong Tian
2023	J	`jnl`	Learning Vision from Models Rivals Learning Vision from Data. CoRR Yonglong Tian, Lijie Fan, Kaifeng Chen, Dina Katabi, Dilip Krishnan, Phillip Isola
2023	J	`jnl`	Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency. CoRR Tianhong Li, Sangnie Bhardwaj, Yonglong Tian, Han Zhang, Jarred Barber, Dina Katabi, Guillaume Lajoie, Huiwen Chang, Dilip Krishnan
2023	A*	`conf`	PFGM++: Unlocking the Potential of Physics-Inspired Generative Models. ICML Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola
2023	J	`jnl`	PFGM++: Unlocking the Potential of Physics-Inspired Generative Models. CoRR Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola
2023	A*	`conf`	Restart Sampling for Improving Generative Processes. NeurIPS Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi S. Jaakkola
2023	J	`jnl`	Restart Sampling for Improving Generative Processes. CoRR Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi S. Jaakkola
2023	J	`jnl`	Scaling Laws of Synthetic Images for Model Training ... for Now. CoRR Lijie Fan, Kaifeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, Yonglong Tian
2023	A*	`conf`	Self-supervision through Random Segments with Autoregressive Coding (RandSAC). ICLR Tianyu Hua, Yonglong Tian, Sucheng Ren, Michalis Raptis, Hang Zhao, Leonid Sigal
2023	A*	`conf`	StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners. NeurIPS Yonglong Tian, Lijie Fan, Phillip Isola, Huiwen Chang, Dilip Krishnan
2023	J	`jnl`	StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners. CoRR Yonglong Tian, Lijie Fan, Phillip Isola, Huiwen Chang, Dilip Krishnan
2023	—	`—`	Towards General-purpose Vision via Multiview Contrastive Learning Yonglong Tian
2022	A*	`conf`	Co-advise: Cross Inductive Bias Distillation. CVPR Sucheng Ren, Zhengqi Gao, Tianyu Hua, Zihui Xue, Yonglong Tian, Shengfeng He, Hang Zhao
2022	J	`jnl`	Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision? CoRR Lirui Wang, Kaiqing Zhang, Yunzhu Li, Yonglong Tian, Russ Tedrake
2022	A*	`conf`	Generative Models as a Data Source for Multiview Representation Learning. ICLR Ali Jahanian, Xavier Puig, Yonglong Tian, Phillip Isola
2022	J	`jnl`	Self-supervision through Random Segments with Autoregressive Coding (RandSAC). CoRR Tianyu Hua, Yonglong Tian, Sucheng Ren, Hang Zhao, Leonid Sigal
2022	A*	`conf`	Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate. AAAI Lu Mi, Hao Wang, Yonglong Tian, Hao He, Nir Shavit
2022	A*	`conf`	Unsupervised Learning of Shape Programs with Repeatable Implicit Parts. NeurIPS Boyang Deng, Sumith Kulal, Zhengyang Dong, Congyue Deng, Yonglong Tian, Jiajun Wu
2021	J	`jnl`	Co-advise: Cross Inductive Bias Distillation. CoRR Sucheng Ren, Zhengqi Gao, Tianyu Hua, Zihui Xue, Yonglong Tian, Shengfeng He, Hang Zhao
2021	A*	`conf`	Composable Augmentation Encoding for Video Representation Learning. ICCV Chen Sun, Arsha Nagrani, Yonglong Tian, Cordelia Schmid
2021	J	`jnl`	Composable Augmentation Encoding for Video Representation Learning. CoRR Chen Sun, Arsha Nagrani, Yonglong Tian, Cordelia Schmid
2021	A*	`conf`	Divide and Contrast: Self-supervised Learning from Uncurated Data. ICCV Yonglong Tian, Olivier J. Hénaff, Aäron van den Oord
2021	J	`jnl`	Divide and Contrast: Self-supervised Learning from Uncurated Data. CoRR Yonglong Tian, Olivier J. Hénaff, Aäron van den Oord
2021	J	`jnl`	Generative Models as a Data Source for Multiview Representation Learning. CoRR Ali Jahanian, Xavier Puig, Yonglong Tian, Phillip Isola
2021	J	`jnl`	Simple Distillation Baselines for Improving Small Self-supervised Models. CoRR Jindong Gu, Wei Liu, Yonglong Tian
2020	—	`conf`	Contrastive Multiview Coding. ECCV (11) Yonglong Tian, Dilip Krishnan, Phillip Isola
2020	A*	`conf`	Contrastive Representation Distillation. ICLR Yonglong Tian, Dilip Krishnan, Phillip Isola
2020	J	`jnl`	Information-Preserving Contrastive Learning for Self-Supervised Representations. CoRR Tianhong Li, Lijie Fan, Yuan Yuan, Hao He, Yonglong Tian, Dina Katabi
2020	—	`conf`	Rethinking Few-Shot Image Classification: A Good Embedding is All You Need? ECCV (14) Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B. Tenenbaum, Phillip Isola
2020	J	`jnl`	Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need? CoRR Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B. Tenenbaum, Phillip Isola
2020	A*	`conf`	Supervised Contrastive Learning. NeurIPS Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, Dilip Krishnan
2020	J	`jnl`	Supervised Contrastive Learning. CoRR Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, Dilip Krishnan
2020	A*	`conf`	What Makes for Good Views for Contrastive Learning? NeurIPS Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola
2020	J	`jnl`	What makes for good views for contrastive learning. CoRR Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola
2019	J	`jnl`	Contrastive Multiview Coding. CoRR Yonglong Tian, Dilip Krishnan, Phillip Isola
2019	J	`jnl`	Contrastive Representation Distillation. CoRR Yonglong Tian, Dilip Krishnan, Phillip Isola
2019	—	`conf`	Learning to Infer and Execute 3D Shape Programs. ICLR (Poster) Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu
2019	J	`jnl`	Learning to Infer and Execute 3D Shape Programs. CoRR Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu
2019	—	`conf`	ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees. ICLR (Poster) Hao He, Hao Wang, Guang-He Lee, Yonglong Tian
2019	J	`jnl`	Training-Free Uncertainty Estimation for Neural Networks. CoRR Lu Mi, Hao Wang, Yonglong Tian, Nir Shavit
2018	J	`jnl`	RF-Based Fall Monitoring Using Convolutional Neural Networks. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Yonglong Tian, Guang-He Lee, Hao He, Chen-Yu Hsu, Dina Katabi
2018	A*	`conf`	RF-based 3D skeletons. SIGCOMM Mingmin Zhao, Yonglong Tian, Hang Zhao, Mohammad Abu Alsheikh, Tianhong Li, Rumen Hristov, Zachary Kabelac, Dina Katabi, Antonio Torralba
2018	A*	`conf`	Representation Learning on Graphs with Jumping Knowledge Networks. ICML Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka
2018	J	`jnl`	Representation Learning on Graphs with Jumping Knowledge Networks. CoRR Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka
2018	A*	`conf`	Through-Wall Human Pose Estimation Using Radio Signals. CVPR Mingmin Zhao, Tianhong Li, Mohammad Abu Alsheikh, Yonglong Tian, Hang Zhao, Antonio Torralba, Dina Katabi
2017	J	`jnl`	DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks. IEEE Trans. Pattern Anal. Mach. Intell. 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
2015	A*	`conf`	Deep Learning Strong Parts for Pedestrian Detection. ICCV Yonglong Tian, Ping Luo, Xiaogang Wang, Xiaoou Tang
2015	A*	`conf`	DeepID-Net: Deformable deep convolutional neural networks for object detection. CVPR Wanli Ouyang, Xiaogang Wang, Xingyu Zeng, Shi Qiu, Ping Luo, Yonglong Tian, Hongsheng Li, Shuo Yang, Zhe Wang, Chen Change Loy, Xiaoou Tang
2015	A*	`conf`	Pedestrian detection aided by deep learning semantic tasks. CVPR Yonglong Tian, Ping Luo, Xiaogang Wang, Xiaoou Tang
2014	J	`jnl`	DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection. CoRR Wanli Ouyang, Xiaogang Wang, Xingyu Zeng, Shi Qiu, Ping Luo, Yonglong Tian, Hongsheng Li, Shuo Yang, Zhe Wang, Chen Change Loy, Xiaoou Tang
2014	J	`jnl`	DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection. CoRR 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
2014	J	`jnl`	Pedestrian Detection aided by Deep Learning Semantic Tasks. CoRR Yonglong Tian, Ping Luo, Xiaogang Wang, Xiaoou Tang
2014	A*	`conf`	Switchable Deep Network for Pedestrian Detection. CVPR Ping Luo, Yonglong Tian, Xiaogang Wang, Xiaoou Tang