16 citations · 51 across the 13 of their papers we have counts for
13 papers
Weakly Supervised Patch Label Inference Networks for Efficient Pavement Distress Detection and Recognition in the Wild
Sheng Huang, Wenhao Tang, Guixin Huang +2
Automatic image-based pavement distress detection and recognition are vital for pavement maintenance and management. However, existing deep learning-based methods largely omit the…
Deep Domain Adaptation for Pavement Crack Detection
Huijun Liu, Chunhua Yang, Ao Li +4
Deep learning-based pavement cracks detection methods often require large-scale labels with detailed crack location information to learn accurate predictions. In practice, however,…
Dizygotic Conditional Variational AutoEncoder for Multi-Modal and Partial Modality Absent Few-Shot Learning
Yi Zhang, Sheng Huang, Xi Peng +1
Data augmentation is a powerful technique for improving the performance of the few-shot classification task. It generates more samples as supplements, and then this task can be tra…
Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network
Zeyu Wang, Sheng Huang, Zhongxin Liu +4
Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visu…
Deep Semantic Dictionary Learning for Multi-label Image Classification
Fengtao Zhou, Sheng Huang, Yun Xing
Compared with single-label image classification, multi-label image classification is more practical and challenging. Some recent studies attempted to leverage the semantic informat…
An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress Detection
Wenhao Tang, Sheng Huang, Qiming Zhao +2
We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement distresses that are n…
Regression-based Hypergraph Learning for Image Clustering and Classification
Sheng Huang, Dan Yang, Bo Liu +1
Inspired by the recently remarkable successes of Sparse Representation (SR), Collaborative Representation (CR) and sparse graph, we present a novel hypergraph model named Regressio…
Learning Hypergraph-regularized Attribute Predictors
Sheng Huang, Mohamed Elhoseiny, Ahmed Elgammal +1
We present a novel attribute learning framework named Hypergraph-based Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the attribute relations in the data. T…
On The Effect of Hyperedge Weights On Hypergraph Learning
Sheng Huang, Ahmed Elgammal, Dan Yang
Hypergraph is a powerful representation in several computer vision, machine learning and pattern recognition problems. In the last decade, many researchers have been keen to develo…
Sparse Graph-based Transduction for Image Classification
Sheng Huang, Dan Yang, Jia Zhou +2
Motivated by the remarkable successes of Graph-based Transduction (GT) and Sparse Representation (SR), we present a novel Classifier named Sparse Graph-based Classifier (SGC) for i…
Collaborative Discriminant Locality Preserving Projections With its Application to Face Recognition
Sheng Huang, Dan Yang, Dong Yang +1
We present a novel Discriminant Locality Preserving Projections (DLPP) algorithm named Collaborative Discriminant Locality Preserving Projection (CDLPP). In our algorithm, the disc…
Shape Primitive Histogram: A Novel Low-Level Face Representation for Face Recognition
Sheng Huang, Dan Yang, Haopeng Zhang +2
We further exploit the representational power of Haar wavelet and present a novel low-level face representation named Shape Primitives Histogram (SPH) for face recognition. Since h…
Face Recognition via Globality-Locality Preserving Projections
Sheng Huang, Dan Yang, Fei Yang +3
We present an improved Locality Preserving Projections (LPP) method, named Gloablity-Locality Preserving Projections (GLPP), to preserve both the global and local geometric structu…