294 citations · 737 across the 61 of their papers we have counts for
7 papers · 1 filter
Masked Modeling for Self-supervised Representation Learning on Vision and Beyond
Siyuan Li, Luyuan Zhang, Zedong Wang +8
As the deep learning revolution marches on, self-supervised learning has garnered increasing attention in recent years thanks to its remarkable representation learning ability and…
USTEP: Spatio-Temporal Predictive Learning under A Unified View
Cheng Tan, Jue Wang, Zhangyang Gao +2
Spatio-temporal predictive learning plays a crucial role in self-supervised learning, with wide-ranging applications across a diverse range of fields. Previous approaches for tempo…
OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning
Cheng Tan, Siyuan Li, Zhangyang Gao +5
Spatio-temporal predictive learning is a learning paradigm that enables models to learn spatial and temporal patterns by predicting future frames from given past frames in an unsup…
SimVP: Simpler yet Better Video Prediction
Zhangyang Gao, Cheng Tan, Lirong Wu +1
From CNN, RNN, to ViT, we have witnessed remarkable advancements in video prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training str…
Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning
Cheng Tan, Zhangyang Gao, Lirong Wu +4
Spatiotemporal predictive learning aims to generate future frames by learning from historical frames. In this paper, we investigate existing methods and present a general framework…
AutoMix: Unveiling the Power of Mixup for Stronger Classifiers
Zicheng Liu, Siyuan Li, Di Wu +4
Data mixing augmentation have proved to be effective in improving the generalization ability of deep neural networks. While early methods mix samples by hand-crafted policies (e.g.…
A Foreground-background Parallel Compression with Residual Encoding for Surveillance Video
Lirong Wu, Kejie Huang, Haibin Shen +1
The data storage has been one of the bottlenecks in surveillance systems. The conventional video compression algorithms such as H.264 and H.265 do not fully utilize the low informa…