3 citations · 6 across the 5 of their papers we have counts for
5 papers
Multi-Dimensional Refinement Graph Convolutional Network with Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition
Sheng-Lan Liu, Yu-Ning Ding, Jin-Rong Zhang +4
Graph convolutional networks have been widely used in skeleton-based action recognition. However, existing approaches are limited in fine-grained action recognition due to the simi…
Color Recognition for Rubik's Cube Robot
Shenglan Liu, Dong Jiang, Lin Feng +6
In this paper, we proposed three methods to solve color recognition of Rubik's cube, which includes one offline method and two online methods. Scatter balance \& extreme learning m…
Rough extreme learning machine: a new classification method based on uncertainty measure
Lin Feng, Shuliang Xu, Feilong Wang +1
Extreme learning machine (ELM) is a new single hidden layer feedback neural network. The weights of the input layer and the biases of neurons in hidden layer are randomly generated…
Feature Fusion using Extended Jaccard Graph and Stochastic Gradient Descent for Robot
Shenglan Liu, Muxin Sun, Wei Wang +1
Robot vision is a fundamental device for human-robot interaction and robot complex tasks. In this paper, we use Kinect and propose a feature graph fusion (FGF) for robot recognitio…
Neural method for Explicit Mapping of Quasi-curvature Locally Linear Embedding in image retrieval
Shenglan Liu, Jun Wu, Lin Feng +1
This paper proposed a new explicit nonlinear dimensionality reduction using neural networks for image retrieval tasks. We first proposed a Quasi-curvature Locally Linear Embedding…