125 citations · 173 across the 12 of their papers we have counts for
9 papers · 1 filter
Adaptive Depth-converted-Scale Convolution for Self-supervised Monocular Depth Estimation
Yanbo Gao, Huibin Bai, Huasong Zhou +6
Self-supervised monocular depth estimation (MDE) has received increasing interests in the last few years. The objects in the scene, including the object size and relationship among…
Monocular Depth Estimation From the Perspective of Feature Restoration: A Diffusion Enhanced Depth Restoration Approach
Huibin Bai, Shuai Li, Hanxiao Zhai +6
Monocular Depth Estimation (MDE) is a fundamental computer vision task with important applications in 3D vision. The current mainstream MDE methods employ an encoder-decoder archit…
LiftFormer: Lifting and Frame Theory Based Monocular Depth Estimation Using Depth and Edge Oriented Subspace Representation
Shuai Li, Huibin Bai, Yanbo Gao +5
Monocular depth estimation (MDE) has attracted increasing interest in the past few years, owing to its important role in 3D vision. MDE is the estimation of a depth map from a mono…
Unsupervised Spatial-Temporal Feature Enrichment and Fidelity Preservation Network for Skeleton based Action Recognition
Chuankun Li, Shuai Li, Yanbo Gao +3
Unsupervised skeleton based action recognition has achieved remarkable progress recently. Existing unsupervised learning methods suffer from severe overfitting problem, and thus sm…
A Two-stream Neural Network for Pose-based Hand Gesture Recognition
Chuankun Li, Shuai Li, Yanbo Gao +2
Pose based hand gesture recognition has been widely studied in the recent years. Compared with full body action recognition, hand gesture involves joints that are more spatially cl…
A Framework of Combining Short-Term Spatial/Frequency Feature Extraction and Long-Term IndRNN for Activity Recognition
Beidi Zhao, Shuai Li, Yanbo Gao +2
Smartphone sensors based human activity recognition is attracting increasing interests nowadays with the popularization of smartphones. With the high sampling rates of smartphone s…
Deep Independently Recurrent Neural Network (IndRNN)
Shuai Li, Wanqing Li, Chris Cook +1
Recurrent neural networks (RNNs) are known to be difficult to train due to the gradient vanishing and exploding problems and thus difficult to learn long-term patterns and construc…
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
Shuai Li, Wanqing Li, Chris Cook +2
Recurrent neural networks (RNNs) have been widely used for processing sequential data. However, RNNs are commonly difficult to train due to the well-known gradient vanishing and ex…
A Fully Trainable Network with RNN-based Pooling
Shuai Li, Wanqing Li, Chris Cook +2
Pooling is an important component in convolutional neural networks (CNNs) for aggregating features and reducing computational burden. Compared with other components such as convolu…