no code implementations • 27 Jun 2023 • Zelun Luo, Yuliang Zou, Yijin Yang, Zane Durante, De-An Huang, Zhiding Yu, Chaowei Xiao, Li Fei-Fei, Animashree Anandkumar
In recent years, differential privacy has seen significant advancements in image classification; however, its application to video activity recognition remains under-explored.
no code implementations • 30 Mar 2022 • Yuliang Zou, Zizhao Zhang, Chun-Liang Li, Han Zhang, Tomas Pfister, Jia-Bin Huang
We propose a test-time adaptation method for cross-domain image segmentation.
1 code implementation • 30 Mar 2021 • Yuliang Zou, Jinwoo Choi, Qitong Wang, Jia-Bin Huang
Data augmentation is a ubiquitous technique for improving image classification when labeled data is scarce.
2 code implementations • ICLR 2021 • Yuliang Zou, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, Tomas Pfister
We demonstrate the effectiveness of the proposed pseudo-labeling strategy in both low-data and high-data regimes.
1 code implementation • ECCV 2020 • Chen Gao, Jiarui Xu, Yuliang Zou, Jia-Bin Huang
We tackle the challenging problem of human-object interaction (HOI) detection.
Ranked #26 on
Human-Object Interaction Detection
on V-COCO
no code implementations • ECCV 2020 • Yuliang Zou, Pan Ji, Quoc-Huy Tran, Jia-Bin Huang, Manmohan Chandraker
Monocular visual odometry (VO) suffers severely from error accumulation during frame-to-frame pose estimation.
1 code implementation • ECCV 2018 • Yuliang Zou, Zelun Luo, Jia-Bin Huang
We present an unsupervised learning framework for simultaneously training single-view depth prediction and optical flow estimation models using unlabeled video sequences.
4 code implementations • 30 Aug 2018 • Chen Gao, Yuliang Zou, Jia-Bin Huang
Our core idea is that the appearance of a person or an object instance contains informative cues on which relevant parts of an image to attend to for facilitating interaction prediction.
Ranked #2 on
Human-Object Interaction Detection
on Ambiguious-HOI
no code implementations • NeurIPS 2017 • Zelun Luo, Yuliang Zou, Judy Hoffman, Li F. Fei-Fei
We propose a framework that learns a representation transferable across different domains and tasks in a data efficient manner.
no code implementations • NeurIPS 2017 • Zelun Luo, Yuliang Zou, Judy Hoffman, Li Fei-Fei
We propose a framework that learns a representation transferable across different domains and tasks in a label efficient manner.
2 code implementations • ICML 2017 • Ruben Villegas, Jimei Yang, Yuliang Zou, Sungryull Sohn, Xunyu Lin, Honglak Lee
To avoid inherent compounding errors in recursive pixel-level prediction, we propose to first estimate high-level structure in the input frames, then predict how that structure evolves in the future, and finally by observing a single frame from the past and the predicted high-level structure, we construct the future frames without having to observe any of the pixel-level predictions.