activity
20152022
most citedTowards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy

52 citations · 69 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CV20222 cited

PPT: token-Pruned Pose Transformer for monocular and multi-view human pose estimation

Haoyu Ma, Zhe Wang, Yifei Chen +6

Recently, the vision transformer and its variants have played an increasingly important role in both monocular and multi-view human pose estimation. Considering image patches as to…

cs.CV2022

The Best of Both Worlds: Combining Model-based and Nonparametric Approaches for 3D Human Body Estimation

Zhe Wang, Jimei Yang, Charless Fowlkes

Nonparametric based methods have recently shown promising results in reconstructing human bodies from monocular images while model-based methods can help correct these estimates an…

cs.LG20212 cited

ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning

Zhe Wang, Jake Grigsby, Arshdeep Sekhon +1

Optimization-based meta-learning typically assumes tasks are sampled from a single distribution - an assumption oversimplifies and limits the diversity of tasks that meta-learning…

cs.LG20212 cited

Reconstructing a dynamical system and forecasting time series by self-consistent deep learning

Zhe Wang, Claude Guet

We introduce a self-consistent deep-learning framework which, for a noisy deterministic time series, provides unsupervised filtering, state-space reconstruction, identification of…

cs.CV202052 cited

Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy

Shuang Xu, Lizhen Ji, Zhe Wang +4

Multi-focus image fusion (MFF) is a popular technique to generate an all-in-focus image, where all objects in the scene are sharp. However, existing methods pay little attention to…

math.OC201911 cited

Momentum Schemes with Stochastic Variance Reduction for Nonconvex Composite Optimization

Yi Zhou, Zhe Wang, Kaiyi Ji +2

Two new stochastic variance-reduced algorithms named SARAH and SPIDER have been recently proposed, and SPIDER has been shown to achieve a near-optimal gradient oracle complexity fo…