219 citations · 509 across the 40 of their papers we have counts for
5 papers · 1 filter
Beyond Lazy Training for Over-parameterized Tensor Decomposition
Xiang Wang, Chenwei Wu, Jason D. Lee +2
Over-parametrization is an important technique in training neural networks. In both theory and practice, training a larger network allows the optimization algorithm to avoid bad lo…
Multi-Level Temporal Pyramid Network for Action Detection
Xiang Wang, Changxin Gao, Shiwei Zhang +1
Currently, one-stage frameworks have been widely applied for temporal action detection, but they still suffer from the challenge that the action instances span a wide range of time…
CBR-Net: Cascade Boundary Refinement Network for Action Detection: Submission to ActivityNet Challenge 2020 (Task 1)
Xiang Wang, Baiteng Ma, Zhiwu Qing +4
In this report, we present our solution for the task of temporal action localization (detection) (task 1) in ActivityNet Challenge 2020. The purpose of this task is to temporally l…
Temporal Fusion Network for Temporal Action Localization:Submission to ActivityNet Challenge 2020 (Task E)
Zhiwu Qing, Xiang Wang, Yongpeng Sang +3
This technical report analyzes a temporal action localization method we used in the HACS competition which is hosted in Activitynet Challenge 2020.The goal of our task is to locate…
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach
Xiang Wang, Shuai Yuan, Chenwei Wu +1
Choosing the right parameters for optimization algorithms is often the key to their success in practice. Solving this problem using a learning-to-learn approach -- using meta-gradi…