584 citations · 1.3k across the 51 of their papers we have counts for
53 papers
Center Focusing Network for Real-Time LiDAR Panoptic Segmentation
Xiaoyan Li, Gang Zhang, Boyue Wang +2
LiDAR panoptic segmentation facilitates an autonomous vehicle to comprehensively understand the surrounding objects and scenes and is required to run in real time. The recent propo…
A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks
Yang Wang, Bo Dong, Ke Xu +4
Deep Neural Networks (DNNs) are widely used for computer vision tasks. However, it has been shown that deep models are vulnerable to adversarial attacks, i.e., their performances d…
Referring Image Segmentation Using Text Supervision
Fang Liu, Yuhao Liu, Yuqiu Kong +5
Existing Referring Image Segmentation (RIS) methods typically require expensive pixel-level or box-level annotations for supervision. In this paper, we observe that the referring t…
Fast Adversarial Training with Smooth Convergence
Mengnan Zhao, Lihe Zhang, Yuqiu Kong +1
Fast adversarial training (FAT) is beneficial for improving the adversarial robustness of neural networks. However, previous FAT work has encountered a significant issue known as c…
A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects
Jiapu Wang, Boyue Wang, Meikang Qiu +8
Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and i…
Frame-Event Alignment and Fusion Network for High Frame Rate Tracking
Jiqing Zhang, Yuanchen Wang, Wenxi Liu +4
Most existing RGB-based trackers target low frame rate benchmarks of around 30 frames per second. This setting restricts the tracker's functionality in the real world, especially f…
ALR-GAN: Adaptive Layout Refinement for Text-to-Image Synthesis
Hongchen Tan, Baocai Yin, Kun Wei +2
We propose a novel Text-to-Image Generation Network, Adaptive Layout Refinement Generative Adversarial Network (ALR-GAN), to adaptively refine the layout of synthesized images with…
Explore Contextual Information for 3D Scene Graph Generation
Yuanyuan Liu, Chengjiang Long, Zhaoxuan Zhang +4
3D scene graph generation (SGG) has been of high interest in computer vision. Although the accuracy of 3D SGG on coarse classification and single relation label has been gradually…
Point Cloud Scene Completion with Joint Color and Semantic Estimation from Single RGB-D Image
Zhaoxuan Zhang, Xiaoguang Han, Bo Dong +3
We present a deep reinforcement learning method of progressive view inpainting for colored semantic point cloud scene completion under volume guidance, achieving high-quality scene…
Monocular Camera-based Complex Obstacle Avoidance via Efficient Deep Reinforcement Learning
Jianchuan Ding, Lingping Gao, Wenxi Liu +5
Deep reinforcement learning has achieved great success in laser-based collision avoidance works because the laser can sense accurate depth information without too much redundant da…
NodeTrans: A Graph Transfer Learning Approach for Traffic Prediction
Xueyan Yin, Feifan Li, Yanming Shen +2
Recently, deep learning methods have made great progress in traffic prediction, but their performance depends on a large amount of historical data. In reality, we may face the data…
Soft-mask: Adaptive Substructure Extractions for Graph Neural Networks
Mingqi Yang, Yanming Shen, Heng Qi +1
For learning graph representations, not all detailed structures within a graph are relevant to the given graph tasks. Task-relevant structures can be or which…
Biologically Inspired Dynamic Thresholds for Spiking Neural Networks
Jianchuan Ding, Bo Dong, Felix Heide +4
The dynamic membrane potential threshold, as one of the essential properties of a biological neuron, is a spontaneous regulation mechanism that maintains neuronal homeostasis, i.e.…
DR-GAN: Distribution Regularization for Text-to-Image Generation
Hongchen Tan, Xiuping Liu, Baocai Yin +1
This paper presents a new Text-to-Image generation model, named Distribution Regularization Generative Adversarial Network (DR-GAN), to generate images from text descriptions from…
Bi-directional Object-context Prioritization Learning for Saliency Ranking
Xin Tian, Ke Xu, Xin Yang +3
The saliency ranking task is recently proposed to study the visual behavior that humans would typically shift their attention over different objects of a scene based on their degre…
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…