1.5k citations · 6k across the 75 of their papers we have counts for
14 papers · 1 filter
Learning Preferences and Demands in Visual Recommendation
Qiang Liu, Shu Wu, Liang Wang
Visual information is an important factor in recommender systems, in which users' selections consist of two components: \emph{preferences} and \emph{demands}. Some studies has been…
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
Fenyu Hu, Yanqiao Zhu, Shu Wu +3
Graph representation learning is of paramount importance for a variety of graph analytical tasks, ranging from node classification to community detection. Recently, graph convoluti…
Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation
Mengqi Zhang, Shu Wu, Meng Gao +3
The problem of session-aware recommendation aims to predict users' next click based on their current session and historical sessions. Existing session-aware recommendation methods…
Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction
Zekun Li, Zeyu Cui, Shu Wu +2
Click-through rate (CTR) prediction is an essential task in web applications such as online advertising and recommender systems, whose features are usually in multi-field form. The…
Learning Efficient Convolutional Networks through Irregular Convolutional Kernels
Weiyu Guo, Jiabin Ma, Liang Wang +1
As deep neural networks are increasingly used in applications suited for low-power devices, a fundamental dilemma becomes apparent: the trend is to grow models to absorb increasing…
Learning Compact Target-Oriented Feature Representations for Visual Tracking
Chenglong Li, Yan Huang, Liang Wang +2
Many state-of-the-art trackers usually resort to the pretrained convolutional neural network (CNN) model for correlation filtering, in which deep features could usually be redundan…
Semi-supervised Compatibility Learning Across Categories for Clothing Matching
Zekun Li, Zeyu Cui, Shu Wu +2
Learning the compatibility between fashion items across categories is a key task in fashion analysis, which can decode the secret of clothing matching. The main idea of this task i…
Improving Description-based Person Re-identification by Multi-granularity Image-text Alignments
Kai Niu, Yan Huang, Wanli Ouyang +1
Description-based person re-identification (Re-id) is an important task in video surveillance that requires discriminative cross-modal representations to distinguish different peop…
Progressive Cluster Purification for Transductive Few-shot Learning
Chenyang Si, Wentao Chen, Wei Wang +2
Few-shot learning aims to learn to generalize a classifier to novel classes with limited labeled data. Transductive inference that utilizes unlabeled test set to deal with low-data…
Richly Activated Graph Convolutional Network for Action Recognition with Incomplete Skeletons
Yi-Fan Song, Zhang Zhang, Liang Wang
Current methods for skeleton-based human action recognition usually work with completely observed skeletons. However, in real scenarios, it is prone to capture incomplete and noisy…
Box-driven Class-wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation
Chunfeng Song, Yan Huang, Wanli Ouyang +1
Semantic segmentation has achieved huge progress via adopting deep Fully Convolutional Networks (FCN). However, the performance of FCN based models severely rely on the amounts of…
An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition
Chenyang Si, Wentao Chen, Wei Wang +2
Skeleton-based action recognition is an important task that requires the adequate understanding of movement characteristics of a human action from the given skeleton sequence. Rece…
Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks
Zeyu Cui, Zekun Li, Shu Wu +2
With the rapid development of fashion market, the customers' demands of customers for fashion recommendation are rising. In this paper, we aim to investigate a practical problem of…
Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification
Fenyu Hu, Yanqiao Zhu, Shu Wu +2
Graph convolutional networks (GCNs) have been successfully applied in node classification tasks of network mining. However, most of these models based on neighborhood aggregation a…