1.5k citations · 6k across the 75 of their papers we have counts for
75 papers
Evidence-aware Fake News Detection with Graph Neural Networks
Weizhi Xu, Junfei Wu, Qiang Liu +2
The prevalence and perniciousness of fake news has been a critical issue on the Internet, which stimulates the development of automatic fake news detection in turn. In this paper,…
Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition
Peng Wang, Jun Wen, Chenyang Si +2
Skeleton-based action recognition is widely used in varied areas, e.g., surveillance and human-machine interaction. Existing models are mainly learned in a supervised manner, thus…
Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation
Jinghao Zhang, Yanqiao Zhu, Qiang Liu +3
Recent years have witnessed growing interests in multimedia recommendation, which aims to predict whether a user will interact with an item with multimodal contents. Previous studi…
Relation-aware Heterogeneous Graph for User Profiling
Qilong Yan, Yufeng Zhang, Qiang Liu +2
User profiling has long been an important problem that investigates user interests in many real applications. Some recent works regard users and their interacted objects as entitie…
Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization
Linjiang Huang, Liang Wang, Hongsheng Li
As a challenging task of high-level video understanding, weakly supervised temporal action localization has been attracting increasing attention. With only video annotations, most…
Fully Hyperbolic Graph Convolution Network for Recommendation
Liping Wang, Fenyu Hu, Shu Wu +1
Recently, Graph Convolution Network (GCN) based methods have achieved outstanding performance for recommendation. These methods embed users and items in Euclidean space, and perfor…
Deep Contrastive Multiview Network Embedding
Mengqi Zhang, Yanqiao Zhu, Qiang Liu +2
Multiview network embedding aims at projecting nodes in the network to low-dimensional vectors, while preserving their multiple relations and attribute information. Contrastive lea…
Neighbor-view Enhanced Model for Vision and Language Navigation
Dong An, Yuankai Qi, Yan Huang +3
Vision and Language Navigation (VLN) requires an agent to navigate to a target location by following natural language instructions. Most of existing works represent a navigation ca…
Adaptive Dilated Convolution For Human Pose Estimation
Zhengxiong Luo, Zhicheng Wang, Yan Huang +3
Most existing human pose estimation (HPE) methods exploit multi-scale information by fusing feature maps of four different spatial sizes, \ie , , , and of th…
CMF: Cascaded Multi-model Fusion for Referring Image Segmentation
Jianhua Yang, Yan Huang, Zhanyu Ma +1
In this work, we address the task of referring image segmentation (RIS), which aims at predicting a segmentation mask for the object described by a natural language expression. Mos…
Constructing Stronger and Faster Baselines 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 recent State-Of-The-Ar…
Representation and Correlation Enhanced Encoder-Decoder Framework for Scene Text Recognition
Mengmeng Cui, Wei Wang, Jinjin Zhang +1
Attention-based encoder-decoder framework is widely used in the scene text recognition task. However, for the current state-of-the-art(SOTA) methods, there is room for improvement…
Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images
Wentao Chen, Chenyang Si, Wei Wang +3
Few-shot learning is a challenging task since only few instances are given for recognizing an unseen class. One way to alleviate this problem is to acquire a strong inductive bias…
End-to-end Alternating Optimization for Blind Super Resolution
Zhengxiong Luo, Yan Huang, Shang Li +2
Previous methods decompose the blind super-resolution (SR) problem into two sequential steps: \textit{i}) estimating the blur kernel from given low-resolution (LR) image and \texti…
FDAN: Flow-guided Deformable Alignment Network for Video Super-Resolution
Jiayi Lin, Yan Huang, Liang Wang
Most Video Super-Resolution (VSR) methods enhance a video reference frame by aligning its neighboring frames and mining information on these frames. Recently, deformable alignment…
DyGCN: Dynamic Graph Embedding with Graph Convolutional Network
Zeyu Cui, Zekun Li, Shu Wu +4
Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes, has received significant attention recently. Recent years have witnessed a surge of eff…
Mining Latent Structures for Multimedia Recommendation
Jinghao Zhang, Yanqiao Zhu, Qiang Liu +3
Multimedia content is of predominance in the modern Web era. Investigating how users interact with multimodal items is a continuing concern within the rapid development of recommen…
Dynamic Graph Neural Networks for Sequential Recommendation
Mengqi Zhang, Shu Wu, Xueli Yu +2
Modeling user preference from his historical sequences is one of the core problems of sequential recommendation. Existing methods in this field are widely distributed from conventi…
Locate then Segment: A Strong Pipeline for Referring Image Segmentation
Ya Jing, Tao Kong, Wei Wang +3
Referring image segmentation aims to segment the objects referred by a natural language expression. Previous methods usually focus on designing an implicit and recurrent feature in…
Graph Classification by Mixture of Diverse Experts
Fenyu Hu, Liping Wang, Shu Wu +2
Graph classification is a challenging research problem in many applications across a broad range of domains. In these applications, it is very common that class distribution is imb…
Learning Domain Invariant Representations for Generalizable Person Re-Identification
Yi-Fan Zhang, Zhang Zhang, Da Li +3
Generalizable person Re-Identification (ReID) has attracted growing attention in recent computer vision community. In this work, we construct a structural causal model among identi…
Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval
Xueli Yu, Weizhi Xu, Zeyu Cui +2
The ad-hoc retrieval task is to rank related documents given a query and a document collection. A series of deep learning based approaches have been proposed to solve such problem…
A Graph-based Relevance Matching Model for Ad-hoc Retrieval
Yufeng Zhang, Jinghao Zhang, Zeyu Cui +2
To retrieve more relevant, appropriate and useful documents given a query, finding clues about that query through the text is crucial. Recent deep learning models regard the task a…
Dynamic Graph Collaborative Filtering
Xiaohan Li, Mengqi Zhang, Shu Wu +3
Dynamic recommendation is essential for modern recommender systems to provide real-time predictions based on sequential data. In real-world scenarios, the popularity of items and i…