116 citations · 430 across the 65 of their papers we have counts for
41 papers · 1 filter
NexusFlow: Unifying Disparate Tasks under Partial Supervision via Invertible Flow Networks
Fangzhou Lin, Yuping Wang, Yuliang Guo +7
Partially Supervised Multi-Task Learning (PS-MTL) aims to leverage knowledge across tasks when annotations are incomplete. Existing approaches, however, have largely focused on the…
A Strong View-Free Baseline Approach for Single-View Image Guided Point Cloud Completion
Fangzhou Lin, Zilin Dai, Rigved Sanku +4
The single-view image guided point cloud completion (SVIPC) task aims to reconstruct a complete point cloud from a partial input with the help of a single-view image. While previou…
Hyperbolic Chamfer Distance for Point Cloud Completion and Beyond
Fangzhou Lin, Songlin Hou, Haotian Liu +4
Chamfer Distance (CD) is widely used as a metric to quantify difference between two point clouds. In point cloud completion, Chamfer Distance (CD) is typically used as a loss funct…
SPADE: Spectroscopic Photoacoustic Denoising using an Analytical and Data-free Enhancement Framework
Fangzhou Lin, Shang Gao, Yichuan Tang +6
Spectroscopic photoacoustic (sPA) imaging uses multiple wavelengths to differentiate chromophores based on their unique optical absorption spectra. This technique has been widely a…
Deep Loss Convexification for Learning Iterative Models
Ziming Zhang, Yuping Shao, Yiqing Zhang +3
Iterative methods such as iterative closest point (ICP) for point cloud registration often suffer from bad local optimality (e.g. saddle points), due to the nature of nonconvex opt…
Robust 3D Point Clouds Classification based on Declarative Defenders
Kaidong Li, Tianxiao Zhang, Cuncong Zhong +2
3D point cloud classification requires distinct models from 2D image classification due to the divergent characteristics of the respective input data. While 3D point clouds are uns…
Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance
Fangzhou Lin, Haotian Liu, Haoying Zhou +6
3D point clouds enhanced the robot's ability to perceive the geometrical information of the environments, making it possible for many downstream tasks such as grasp pose detection…
Understanding Hyperbolic Metric Learning through Hard Negative Sampling
Yun Yue, Fangzhou Lin, Guanyi Mou +1
In recent years, there has been a growing trend of incorporating hyperbolic geometry methods into computer vision. While these methods have achieved state-of-the-art performance on…
PRISE: Demystifying Deep Lucas-Kanade with Strongly Star-Convex Constraints for Multimodel Image Alignment
Yiqing Zhang, Xinming Huang, Ziming Zhang
The Lucas-Kanade (LK) method is a classic iterative homography estimation algorithm for image alignment, but often suffers from poor local optimality especially when image pairs ha…
Self-supervised Geometric Features Discovery via Interpretable Attentio for Vehicle Re-Identification and Beyond (Complete Version)
Ming Li, Xinming Huang, Ziming Zhang
To learn distinguishable patterns, most of recent works in vehicle re-identification (ReID) struggled to redevelop official benchmarks to provide various supervisions, which requir…
Hyperbolic Contrastive Learning
Yun Yue, Fangzhou Lin, Kazunori D Yamada +1
Learning good image representations that are beneficial to downstream tasks is a challenging task in computer vision. As such, a wide variety of self-supervised learning approaches…
EgoSpeed-Net: Forecasting Speed-Control in Driver Behavior from Egocentric Video Data
Yichen Ding, Ziming Zhang, Yanhua Li +1
Speed-control forecasting, a challenging problem in driver behavior analysis, aims to predict the future actions of a driver in controlling vehicle speed such as braking or acceler…
Robust Object Detection With Inaccurate Bounding Boxes
Chengxin Liu, Kewei Wang, Hao Lu +2
Learning accurate object detectors often requires large-scale training data with precise object bounding boxes. However, labeling such data is expensive and time-consuming. As the…
Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks with Implicit Gradients
Kaidong Li, Ziming Zhang, Cuncong Zhong +1
Deep neural networks for 3D point cloud classification, such as PointNet, have been demonstrated to be vulnerable to adversarial attacks. Current adversarial defenders often learn…
CoFi: Coarse-to-Fine ICP for LiDAR Localization in an Efficient Long-lasting Point Cloud Map
Yecheng Lyu, Xinming Huang, Ziming Zhang
LiDAR odometry and localization has attracted increasing research interest in recent years. In the existing works, iterative closest point (ICP) is widely used since it is precise…
Revisiting 2D Convolutional Neural Networks for Graph-based Applications
Yecheng Lyu, Xinming Huang, Ziming Zhang
Graph convolutional networks (GCNs) are widely used in graph-based applications such as graph classification and segmentation. However, current GCNs have limitations on implementat…
Deep Lucas-Kanade Homography for Multimodal Image Alignment
Yiming Zhao, Xinming Huang, Ziming Zhang
Estimating homography to align image pairs captured by different sensors or image pairs with large appearance changes is an important and general challenge for many computer vision…
A Surface Geometry Model for LiDAR Depth Completion
Yiming Zhao, Lin Bai, Ziming Zhang +1
LiDAR depth completion is a task that predicts depth values for every pixel on the corresponding camera frame, although only sparse LiDAR points are available. Most of the existing…
Training Deep Neural Networks via Branch-and-Bound
Yuanwei Wu, Ziming Zhang, Guanghui Wang
In this paper, we propose BPGrad, a novel approximate algorithm for deep nueral network training, based on adaptive estimates of feasible region via branch-and-bound. The method is…
EllipsoidNet: Ellipsoid Representation for Point Cloud Classification and Segmentation
Yecheng Lyu, Xinming Huang, Ziming Zhang
Point cloud patterns are hard to learn because of the implicit local geometry features among the orderless points. In recent years, point cloud representation in 2D space has attra…
Aerial Height Prediction and Refinement Neural Networks with Semantic and Geometric Guidance
Elhousni Mahdi, Zhang Ziming, Huang Xinming
Deep learning provides a powerful new approach to many computer vision tasks. Height prediction from aerial images is one of those tasks that benefited greatly from the deployment…
Self-supervised Geometric Features Discovery via Interpretable Attention for Vehicle Re-Identification and Beyond
Ming Li, Xinming Huang, Ziming Zhang
To learn distinguishable patterns, most of recent works in vehicle re-identification (ReID) struggled to redevelop official benchmarks to provide various supervisions, which requir…
LodoNet: A Deep Neural Network with 2D Keypoint Matchingfor 3D LiDAR Odometry Estimation
Ce Zheng, Yecheng Lyu, Ming Li +1
Deep learning based LiDAR odometry (LO) estimation attracts increasing research interests in the field of autonomous driving and robotics. Existing works feed consecutive LiDAR fra…
TreeRNN: Topology-Preserving Deep GraphEmbedding and Learning
Yecheng Lyu, Ming Li, Xinming Huang +3
General graphs are difficult for learning due to their irregular structures. Existing works employ message passing along graph edges to extract local patterns using customized grap…