584 citations · 1.3k across the 51 of their papers we have counts for
13 papers · 1 filter
A New Perspective on the Effects of Spectrum in Graph Neural Networks
Mingqi Yang, Yanming Shen, Rui Li +3
Many improvements on GNNs can be deemed as operations on the spectrum of the underlying graph matrix, which motivates us to directly study the characteristics of the spectrum and t…
Learning to Detect Instance-level Salient Objects Using Complementary Image Labels
Xin Tian, Ke Xu, Xin Yang +2
Existing salient instance detection (SID) methods typically learn from pixel-level annotated datasets. In this paper, we present the first weakly-supervised approach to the SID pro…
Temporal Knowledge Graph Reasoning Triggered by Memories
Mengnan Zhao, Lihe Zhang, Yuqiu Kong +1
Inferring missing facts in temporal knowledge graphs is a critical task and has been widely explored. Extrapolation in temporal reasoning tasks is more challenging and gradually at…
Object Tracking by Jointly Exploiting Frame and Event Domain
Jiqing Zhang, Xin Yang, Yingkai Fu +3
Inspired by the complementarity between conventional frame-based and bio-inspired event-based cameras, we propose a multi-modal based approach to fuse visual cues from the frame- a…
Grassmannian Graph-attentional Landmark Selection for Domain Adaptation
Bin Sun, Shaofan Wang, Dehui Kong +2
Domain adaptation aims to leverage information from the source domain to improve the classification performance in the target domain. It mainly utilizes two schemes: sample reweigh…
A Vision-based Irregular Obstacle Avoidance Framework via Deep Reinforcement Learning
Lingping Gao, Jianchuan Ding, Wenxi Liu +4
Deep reinforcement learning has achieved great success in laser-based collision avoidance work because the laser can sense accurate depth information without too much redundant dat…
Multi-domain Collaborative Feature Representation for Robust Visual Object Tracking
Jiqing Zhang, Kai Zhao, Bo Dong +4
Jointly exploiting multiple different yet complementary domain information has been proven to be an effective way to perform robust object tracking. This paper focuses on effective…
GAN for Vision, KG for Relation: a Two-stage Deep Network for Zero-shot Action Recognition
Bin Sun, Dehui Kong, Shaofan Wang +3
Zero-shot action recognition can recognize samples of unseen classes that are unavailable in training by exploring common latent semantic representation in samples. However, most m…
Real-time Human Action Recognition Using Locally Aggregated Kinematic-Guided Skeletonlet and Supervised Hashing-by-Analysis Model
Bin Sun, Shaofan Wang, Dehui Kong +2
3D action recognition is referred to as the classification of action sequences which consist of 3D skeleton joints. While many research work are devoted to 3D action recognition, i…
A Two-Stage Attentive Network for Single Image Super-Resolution
Jiqing Zhang, Chengjiang Long, Yuxin Wang +4
Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution (SISR) and contribute remarkable progress. However, most of the exist…
Smart Scribbles for Image Mating
Xin Yang, Yu Qiao, Shaozhe Chen +5
Image matting is an ill-posed problem that usually requires additional user input, such as trimaps or scribbles. Drawing a fne trimap requires a large amount of user effort, while…
Automatic Comic Generation with Stylistic Multi-page Layouts and Emotion-driven Text Balloon Generation
Xin Yang, Zongliang Ma, Letian Yu +5
In this paper, we propose a fully automatic system for generating comic books from videos without any human intervention. Given an input video along with its subtitles, our approac…
CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network for Clustering
Guangyu Huo, Yong Zhang, Junbin Gao +3
With the powerful learning ability of deep convolutional networks, deep clustering methods can extract the most discriminative information from individual data and produce more sat…