Manli Shu

According to our database, Manli Shu authored at least 22 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations.

Can Qin, Congying Xia, Krithika Ramakrishnan, Michael S. Ryoo, Lifu Tu, Yihao Feng, Manli Shu, Honglu Zhou, Anas Awadalla, Jun Wang, Senthil Purushwalkam, Le Xue, Yingbo Zhou, Huan Wang, Silvio Savarese, Juan Carlos Niebles, Zeyuan Chen, Ran Xu, Caiming Xiong

CoRR, 2024

xGen-MM (BLIP-3): A Family of Open Large Multimodal Models.

Le Xue, Manli Shu, Anas Awadalla, Jun Wang, An Yan, Senthil Purushwalkam, Honglu Zhou, Viraj Prabhu, Yutong Dai, Michael S. Ryoo, Shrikant Kendre, Jieyu Zhang, Can Qin, Shu Zhang, Chia-Chih Chen, Ning Yu, Juntao Tan, Tulika Manoj Awalgaonkar, Shelby Heinecke, Huan Wang, Yejin Choi, Ludwig Schmidt, Zeyuan Chen, Silvio Savarese, Juan Carlos Niebles, Caiming Xiong, Ran Xu

CoRR, 2024

MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens.

Anas Awadalla, Le Xue, Oscar Lo, Manli Shu, Hannah Lee, Etash Kumar Guha, Matt Jordan, Sheng Shen, Mohamed Awadalla, Silvio Savarese, Caiming Xiong, Ran Xu, Yejin Choi, Ludwig Schmidt

CoRR, 2024

Coercing LLMs to do and reveal (almost) anything.

Jonas Geiping, Alex Stein, Manli Shu, Khalid Saifullah, Yuxin Wen, Tom Goldstein

CoRR, 2024

Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models.

Yuancheng Xu, Jiarui Yao, Manli Shu, Yanchao Sun, Zichu Wu, Ning Yu, Tom Goldstein, Furong Huang

CoRR, 2024

Hierarchical Point Attention for Indoor 3D Object Detection.

Manli Shu, Le Xue, Ning Yu, Roberto Martín-Martín, Caiming Xiong, Tom Goldstein, Juan Carlos Niebles, Ran Xu

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

On the Reliability of Watermarks for Large Language Models.

John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, Tom Goldstein

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Bring Your Own Data! Self-Supervised Evaluation for Large Language Models.

Neel Jain, Khalid Saifullah, Yuxin Wen, John Kirchenbauer, Manli Shu, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein

CoRR, 2023

Model-Agnostic Hierarchical Attention for 3D Object Detection.

Manli Shu, Le Xue, Ning Yu, Roberto Martín-Martín, Juan Carlos Niebles, Caiming Xiong, Ran Xu

CoRR, 2023

On the Exploitability of Instruction Tuning.

Manli Shu, Jiongxiao Wang, Chen Zhu, Jonas Geiping, Chaowei Xiao, Tom Goldstein

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks.

Micah Goldblum, Hossein Souri, Renkun Ni, Manli Shu, Viraj Prabhu, Gowthami Somepalli, Prithvijit Chattopadhyay, Mark Ibrahim, Adrien Bardes, Judy Hoffman, Rama Chellappa, Andrew Gordon Wilson, Tom Goldstein

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

What do Vision Transformers Learn? A Visual Exploration.

Amin Ghiasi, Hamid Kazemi, Eitan Borgnia, Steven Reich, Manli Shu, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein

CoRR, 2022

Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models.

Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, Chaowei Xiao

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability.

Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

The Close Relationship Between Contrastive Learning and Meta-Learning.

Renkun Ni, Manli Shu, Hossein Souri, Micah Goldblum, Tom Goldstein

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images.

Yu Shen, Laura Y. Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming C. Lin

CoRR, 2021

Encoding Robustness to Image Style via Adversarial Feature Perturbations.

Manli Shu, Zuxuan Wu, Micah Goldblum, Tom Goldstein

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering.

Yu Shen, Laura Y. Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming C. Lin

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Adversarial Differentiable Data Augmentation for Autonomous Systems.

Manli Shu, Yu Shen, Ming C. Lin, Tom Goldstein

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

2020

Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer.

Chen Zhu, Zheng Xu, Ali Shafahi, Manli Shu, Amin Ghiasi, Tom Goldstein

CoRR, 2020

Prepare for the Worst: Generalizing across Domain Shifts with Adversarial Batch Normalization.

Manli Shu, Zuxuan Wu, Micah Goldblum, Tom Goldstein

CoRR, 2020

Headless Horseman: Adversarial Attacks on Transfer Learning Models.

Ahmed Abdelkader, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, Chen Zhu

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020