20 citations · 75 across the 18 of their papers we have counts for
18 papers
End-to-End Streaming Video Temporal Action Segmentation with Reinforce Learning
Jinrong Zhang, Wujun Wen, Shenglan Liu +3
The streaming temporal action segmentation (STAS) task, a supplementary task of temporal action segmentation (TAS), has not received adequate attention in the field of video unders…
Deep graph convolution neural network with non-negative matrix factorization for community discovery
Shuliang Xu, Shenglan Liu, Lin Feng
Community discovery is an important task for graph mining. Owing to the nonstructure, the high dimensionality, and the sparsity of graph data, it is not easy to obtain an appropria…
Self-Supervised Deep Graph Embedding with High-Order Information Fusion for Community Discovery
Shuliang Xu, Shenglan Liu, Lin Feng
Deep graph embedding is an important approach for community discovery. Deep graph neural network with self-supervised mechanism can obtain the low-dimensional embedding vectors of…
Angular Embedding: A New Angular Robust Principal Component Analysis
Shenglan Liu, Yang Yu
As a widely used method in machine learning, principal component analysis (PCA) shows excellent properties for dimensionality reduction. It is a serious problem that PCA is sensiti…
Local Neighbor Propagation Embedding
Shenglan Liu, Yang Yu
Manifold Learning occupies a vital role in the field of nonlinear dimensionality reduction and its ideas also serve for other relevant methods. Graph-based methods such as Graph Co…
Hierarchic Neighbors Embedding
Shenglan Liu, Yang Yu, Yang Liu +3
Manifold learning now plays a very important role in machine learning and many relevant applications. Although its superior performance in dealing with nonlinear data distribution,…
A fast online cascaded regression algorithm for face alignment
Lin Feng, Caifeng Liu, Shenglan Liu +1
Traditional face alignment based on machine learning usually tracks the localizations of facial landmarks employing a static model trained offline where all of the training data is…
Bottom-up Broadcast Neural Network For Music Genre Classification
Caifeng Liu, Lin Feng, Guochao Liu +2
Music genre recognition based on visual representation has been successfully explored over the last years. Recently, there has been increasing interest in attempting convolutional…
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…
Multi-view Laplacian Eigenmaps Based on Bag-of-Neighbors For RGBD Human Emotion Recognition
Shenglan Liu, Shuai Guo, Hong Qiao +6
Human emotion recognition is an important direction in the field of biometric and information forensics. However, most existing human emotion research are based on the single RGB v…
Perceptual Visual Interactive Learning
Shenglan Liu, Xiang Liu, Yang Liu +4
Supervised learning methods are widely used in machine learning. However, the lack of labels in existing data limits the application of these technologies. Visual interactive learn…
Music Genre Classification with Paralleling Recurrent Convolutional Neural Network
Lin Feng, Shenlan Liu, Jianing Yao
Deep learning has been demonstrated its effectiveness and efficiency in music genre classification. However, the existing achievements still have several shortcomings which impair…
Hand Gesture Recognition with Leap Motion
Youchen Du, Shenglan Liu, Lin Feng +2
The recent introduction of depth cameras like Leap Motion Controller allows researchers to exploit the depth information to recognize hand gesture more robustly. This paper propose…
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…
Perceptual uniform descriptor and Ranking on manifold: A bridge between image representation and ranking for image retrieval
Shenglan Liu, Jun Wu, Lin Feng +4
Incompatibility of image descriptor and ranking is always neglected in image retrieval. In this paper, manifold learning and Gestalt psychology theory are involved to solve the inc…
Three Tiers Neighborhood Graph and Multi-graph Fusion Ranking for Multi-feature Image Retrieval: A Manifold Aspect
Shenglan Liu, Muxin Sun, Lin Feng +2
Single feature is inefficient to describe content of an image, which is a shortcoming in traditional image retrieval task. We know that one image can be described by different feat…