activity
20182022
most citedUnsupervised Transfer Learning for Spatiotemporal Predictive Networks

7 citations · 12 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV2022

MetaSets: Meta-Learning on Point Sets for Generalizable Representations

Chao Huang, Zhangjie Cao, Yunbo Wang +2

Deep learning techniques for point clouds have achieved strong performance on a range of 3D vision tasks. However, it is costly to annotate large-scale point sets, making it critic…

cs.LG20211 cited

ModeRNN: Harnessing Spatiotemporal Mode Collapse in Unsupervised Predictive Learning

Zhiyu Yao, Yunbo Wang, Haixu Wu +2

Learning predictive models for unlabeled spatiotemporal data is challenging in part because visual dynamics can be highly entangled in real scenes, making existing approaches prone…

cs.LG20207 cited

Unsupervised Transfer Learning for Spatiotemporal Predictive Networks

Zhiyu Yao, Yunbo Wang, Mingsheng Long +1

This paper explores a new research problem of unsupervised transfer learning across multiple spatiotemporal prediction tasks. Unlike most existing transfer learning methods that fo…

cs.CV20194 cited

Spatiotemporal Pyramid Network for Video Action Recognition

Yunbo Wang, Mingsheng Long, Jianmin Wang +1

Two-stream convolutional networks have shown strong performance in video action recognition tasks. The key idea is to learn spatiotemporal features by fusing convolutional networks…

cs.CV2018

Multi-Task Learning of Generalizable Representations for Video Action Recognition

Zhiyu Yao, Yunbo Wang, Mingsheng Long +3

In classic video action recognition, labels may not contain enough information about the diverse video appearance and dynamics, thus, existing models that are trained under the sta…

cs.LG2018

Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics

Yunbo Wang, Jianjin Zhang, Hongyu Zhu +3

Natural spatiotemporal processes can be highly non-stationary in many ways, e.g. the low-level non-stationarity such as spatial correlations or temporal dependencies of local pixel…