74 citations · 304 across the 31 of their papers we have counts for
32 papers
You Only Look Bottom-Up for Monocular 3D Object Detection
Kaixin Xiong, Dingyuan Zhang, Dingkang Liang +5
Monocular 3D Object Detection is an essential task for autonomous driving. Meanwhile, accurate 3D object detection from pure images is very challenging due to the loss of depth inf…
Diffusion-based 3D Object Detection with Random Boxes
Xin Zhou, Jinghua Hou, Tingting Yao +6
3D object detection is an essential task for achieving autonomous driving. Existing anchor-based detection methods rely on empirical heuristics setting of anchors, which makes the…
RLSAC: Reinforcement Learning enhanced Sample Consensus for End-to-End Robust Estimation
Chang Nie, Guangming Wang, Zhe Liu +3
Robust estimation is a crucial and still challenging task, which involves estimating model parameters in noisy environments. Although conventional sampling consensus-based algorith…
End-to-end 2D-3D Registration between Image and LiDAR Point Cloud for Vehicle Localization
Guangming Wang, Yu Zheng, Yuxuan Wu +5
Robot localization using a built map is essential for a variety of tasks including accurate navigation and mobile manipulation. A popular approach to robot localization is based on…
SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model
Dingyuan Zhang, Dingkang Liang, Hongcheng Yang +4
With the development of large language models, many remarkable linguistic systems like ChatGPT have thrived and achieved astonishing success on many tasks, showing the incredible p…
E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition
Zhen Zhang, Mengting Hu, Shiwan Zhao +6
Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER syste…
Multi-Modal 3D Object Detection by Box Matching
Zhe Liu, Xiaoqing Ye, Zhikang Zou +5
Multi-modal 3D object detection has received growing attention as the information from different sensors like LiDAR and cameras are complementary. Most fusion methods for 3D detect…
RARE: Robust Masked Graph Autoencoder
Wenxuan Tu, Qing Liao, Sihang Zhou +5
Masked graph autoencoder (MGAE) has emerged as a promising self-supervised graph pre-training (SGP) paradigm due to its simplicity and effectiveness. However, existing efforts perf…
Mode-locking Theory for Long-Range Interaction in Artificial Neural Networks
Xiuxiu Bai, Shuaishuai Zhao, Yao Gao +1
Visual long-range interaction refers to modeling dependencies between distant feature points or blocks within an image, which can significantly enhance the model's robustness. Both…
DDS3D: Dense Pseudo-Labels with Dynamic Threshold for Semi-Supervised 3D Object Detection
Jingyu Li, Zhe Liu, Jinghua Hou +1
In this paper, we present a simple yet effective semi-supervised 3D object detector named DDS3D. Our main contributions have two-fold. On the one hand, different from previous work…
RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration
Jiuming Liu, Guangming Wang, Zhe Liu +3
Although point cloud registration has achieved remarkable advances in object-level and indoor scenes, large-scale registration methods are rarely explored. Challenges mainly arise…
Efficient and Low Overhead Website Fingerprinting Attacks and Defenses based on TCP/IP Traffic
Guodong Huang, Chuan Ma, Ming Ding +4
Website fingerprinting attack is an extensively studied technique used in a web browser to analyze traffic patterns and thus infer confidential information about users. Several web…
Hilbert Distillation for Cross-Dimensionality Networks
Dian Qin, Haishuai Wang, Zhe Liu +3
3D convolutional neural networks have revealed superior performance in processing volumetric data such as video and medical imaging. However, the competitive performance by leverag…
PromptAttack: Prompt-based Attack for Language Models via Gradient Search
Yundi Shi, Piji Li, Changchun Yin +3
As the pre-trained language models (PLMs) continue to grow, so do the hardware and data requirements for fine-tuning PLMs. Therefore, the researchers have come up with a lighter me…
What Matters for 3D Scene Flow Network
Guangming Wang, Yunzhe Hu, Zhe Liu +4
3D scene flow estimation from point clouds is a low-level 3D motion perception task in computer vision. Flow embedding is a commonly used technique in scene flow estimation, and it…
SparseDet: Towards End-to-End 3D Object Detection
Jianhong Han, Zhaoyi Wan, Zhe Liu +2
In this paper, we propose SparseDet for end-to-end 3D object detection from point cloud. Existing works on 3D object detection rely on dense object candidates over all locations in…
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Bingzhe Wu, Jintang Li, Junchi Yu +17
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite t…
Emergence of Double-slit Interference by Representing Visual Space in Artificial Neural Networks
Xiuxiu Bai, Zhe Liu, Yao Gao +2
Artificial neural networks have realized incredible successes at image recognition, but the underlying mechanism of visual space representation remains a huge mystery. Grid cells (…
EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection
Zhe Liu, Tengteng Huang, Bingling Li +3
Recently, fusing the LiDAR point cloud and camera image to improve the performance and robustness of 3D object detection has received more and more attention, as these two modaliti…
Toward Native Artificial Intelligence in 6G Networks: System Design, Architectures, and Paradigms
Jianjun Wu, Rongpeng Li, Xueli An +4
The mobile communication system has transformed to be the fundamental infrastructure to support digital demands from all industry sectors, and 6G is envisioned to go far beyond the…
GAKP: GRU Association and Kalman Prediction for Multiple Object Tracking
Zhen Li, Sunzeng Cai, Xiaoyi Wang +2
Multiple Object Tracking (MOT) has been a useful yet challenging task in many real-world applications such as video surveillance, intelligent retail, and smart city. The challenge…
ProMask: Probability Mask for Skeleton Detection
Xiuxiu Bai, Lele Ye, Zhe Liu
Detecting object skeletons in natural images presents challenging, due to varied object scales, the complexity of backgrounds and various noises. The skeleton is a highly compressi…
Anchor-Based Spatio-Temporal Attention 3D Convolutional Networks for Dynamic 3D Point Cloud Sequences
Guangming Wang, Muyao Chen, Hanwen Liu +3
With the rapid development of measurement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perce…
A Registration-aided Domain Adaptation Network for 3D Point Cloud Based Place Recognition
Zhijian Qiao, Hanjiang Hu, Weiang Shi +3
In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness…