1.5k citations · 6k across the 75 of their papers we have counts for
19 papers · 1 filter
Efficient Human Pose Estimation by Learning Deeply Aggregated Representations
Zhengxiong Luo, Zhicheng Wang, Yuanhao Cai +6
In this paper, we propose an efficient human pose estimation network (DANet) by learning deeply aggregated representations. Most existing models explore multi-scale information mai…
Unfolding the Alternating Optimization for Blind Super Resolution
Zhengxiong Luo, Yan Huang, Shang Li +2
Previous methods decompose blind super resolution (SR) problem into two sequential steps: \textit{i}) estimating blur kernel from given low-resolution (LR) image and \textit{ii}) r…
Heterogeneous Graph Collaborative Filtering
Zekun Li, Yujia Zheng, Shu Wu +2
Graph-based collaborative filtering (CF) algorithms have gained increasing attention. Existing work in this literature usually models the user-item interactions as a bipartite grap…
Actor and Action Modular Network for Text-based Video Segmentation
Jianhua Yang, Yan Huang, Kai Niu +3
Text-based video segmentation aims to segment an actor in video sequences by specifying the actor and its performing action with a textual query. Previous methods fail to explicitl…
Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action Recognition
Yi-Fan Song, Zhang Zhang, Caifeng Shan +1
One essential problem in skeleton-based action recognition is how to extract discriminative features over all skeleton joints. However, the complexity of the State-Of-The-Art (SOTA…
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision
Yanqiao Zhu, Weizhi Xu, Qiang Liu +1
This paper studies active learning (AL) on graphs, whose purpose is to discover the most informative nodes to maximize the performance of graph neural networks (GNNs). Previously,…
Graph Contrastive Learning with Adaptive Augmentation
Yanqiao Zhu, Yichen Xu, Feng Yu +3
Recently, contrastive learning (CL) has emerged as a successful method for unsupervised graph representation learning. Most graph CL methods first perform stochastic augmentation o…
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning
Yanqiao Zhu, Yichen Xu, Feng Yu +2
Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive feat…
Tasks Integrated Networks: Joint Detection and Retrieval for Image Search
Lei Zhang, Zhenwei He, Yi Yang +2
The traditional object retrieval task aims to learn a discriminative feature representation with intra-similarity and inter-dissimilarity, which supposes that the objects in an ima…
Recurrent Deconvolutional Generative Adversarial Networks with Application to Text Guided Video Generation
Hongyuan Yu, Yan Huang, Lihong Pi +1
This paper proposes a novel model for video generation and especially makes the attempt to deal with the problem of video generation from text descriptions, i.e., synthesizing real…
Disentangled Item Representation for Recommender Systems
Zeyu Cui, Feng Yu, Shu Wu +2
Item representations in recommendation systems are expected to reveal the properties of items. Collaborative recommender methods usually represent an item as one single latent vect…
Richly Activated Graph Convolutional Network for Robust Skeleton-based Action Recognition
Yi-Fan Song, Zhang Zhang, Caifeng Shan +1
Current methods for skeleton-based human action recognition usually work with complete skeletons. However, in real scenarios, it is inevitable to capture incomplete or noisy skelet…
Deep Graph Contrastive Representation Learning
Yanqiao Zhu, Yichen Xu, Feng Yu +3
Graph representation learning nowadays becomes fundamental in analyzing graph-structured data. Inspired by recent success of contrastive methods, in this paper, we propose a novel…
Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition
Chenyang Si, Xuecheng Nie, Wei Wang +3
We consider the problem of semi-supervised 3D action recognition which has been rarely explored before. Its major challenge lies in how to effectively learn motion representations…
On Learning Semantic Representations for Million-Scale Free-Hand Sketches
Peng Xu, Yongye Huang, Tongtong Yuan +4
In this paper, we study learning semantic representations for million-scale free-hand sketches. This is highly challenging due to the domain-unique traits of sketches, e.g., divers…
TFNet: Multi-Semantic Feature Interaction for CTR Prediction
Shu Wu, Feng Yu, Xueli Yu +5
The CTR (Click-Through Rate) prediction plays a central role in the domain of computational advertising and recommender systems. There exists several kinds of methods proposed in t…
Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks
Yufeng Zhang, Xueli Yu, Zeyu Cui +3
Text classification is fundamental in natural language processing (NLP), and Graph Neural Networks (GNN) are recently applied in this task. However, the existing graph-based works…
TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
Feng Yu, Yanqiao Zhu, Qiang Liu +3
Session-based recommendation nowadays plays a vital role in many websites, which aims to predict users' actions based on anonymous sessions. There have emerged many studies that mo…
Deep Self-Supervised Representation Learning for Free-Hand Sketch
Peng Xu, Zeyu Song, Qiyue Yin +2
In this paper, we tackle for the first time, the problem of self-supervised representation learning for free-hand sketches. This importantly addresses a common problem faced by the…