Search Results for author:
Found 38 papers, 18 papers with code
Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network
In this paper, we present a Memory Prior Contrastive Network (MPCN) that can store shape prior knowledge in a few-shot learning based 3D reconstruction framework.
FewSOL: A Dataset for Few-Shot Object Learning in Robotic Environments
We introduce the Few-Shot Object Learning (FewSOL) dataset for object recognition with a few images per object.
A Manifold-based Airfoil Geometric-feature Extraction and Discrepant Data Fusion Learning Method
Geometrical shape of airfoils, together with the corresponding flight conditions, are crucial factors for aerodynamic performances prediction.
HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers
We analyze the performance of a set of baselines and show a correlation with a real-world evaluation.
An Invariant Matching Property for Distribution Generalization under Intervened Response
The task of distribution generalization concerns making reliable prediction of a response in unseen environments.
A Multi-Characteristic Learning Method with Micro-Doppler Signatures for Pedestrian Identification
The identification of pedestrians using radar micro-Doppler signatures has become a hot topic in recent years.
Variable Selection with the Knockoffs: Composite Null Hypotheses
The Fixed-X knockoff filter is a flexible framework for variable selection with false discovery rate (FDR) control in linear models with arbitrary (non-singular) design matrices and it allows for finite-sample selective inference via the LASSO estimates.
iCaps: Iterative Category-level Object Pose and Shape Estimation
The auto-encoder can be used in a particle filter framework to estimate and track 6D poses of objects in a category.
TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual Information
We propose TALISMAN, a novel framework for Targeted Active Learning or object detectIon with rare slices using Submodular MutuAl iNformation.
Differentially Private Variable Selection via the Knockoff Filter
The knockoff filter, recently developed by Barber and Candes, is an effective procedure to perform variable selection with a controlled false discovery rate (FDR).
A Subsampling-Based Method for Causal Discovery on Discrete Data
Inferring causal directions on discrete and categorical data is an important yet challenging problem.
RICE: Refining Instance Masks in Cluttered Environments with Graph Neural Networks
We postulate that a network architecture that encodes relations between objects at a high-level can be beneficial.
DexYCB: A Benchmark for Capturing Hand Grasping of Objects
We introduce DexYCB, a new dataset for capturing hand grasping of objects.
RGB-D Local Implicit Function for Depth Completion of Transparent Objects
Key to our approach is a local implicit neural representation built on ray-voxel pairs that allows our method to generalize to unseen objects and achieve fast inference speed.
Deterministic distribution of multipartite entanglement and steering in a quantum network by separable states
We experimentally demonstrate the deterministic distribution of two- and three-mode Gaussian entanglement and steering by transmitting separable states in a network consisting of a quantum server and multiple users.
Quantum Physics
Causal Inference from Slowly Varying Nonstationary Processes
Causal inference from observational data following the restricted structural causal model (SCM) framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms, such as non-Gaussianity or nonlinearity.
Aerodynamic Data Predictions Based on Multi-task Learning
Predicting high-speed data is more difficult than predicting low-speed data, owing to that the number of high-speed data is limited, i. e. the quality of the Burgers' dataset is not satisfactory.
Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds
We demonstrate that our learned policy can be integrated into a tabletop 6D grasping system and a human-robot handover system to improve the grasping performance of unseen objects.
Information Laundering for Model Privacy
In this work, we propose information laundering, a novel framework for enhancing model privacy.
Flow Field Reconstructions with GANs based on Radial Basis Functions
Motivated by the problems of existing approaches and inspired by the success of the generative adversarial networks (GANs) in the field of computer vision, we prove an optimal discriminator theorem that the optimal discriminator of a GAN is a radial basis function neural network (RBFNN) while dealing with nonlinear sparse FFD regression and generation.
Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation
In this work, we propose a new method for unseen object instance segmentation by learning RGB-D feature embeddings from synthetic data.
Unseen Object Instance Segmentation for Robotic Environments
We also show that our method can segment unseen objects for robot grasping.
LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation
We evaluate the performance of our method for unseen object pose estimation on MOPED as well as the ModelNet and LINEMOD datasets.
Manipulation Trajectory Optimization with Online Grasp Synthesis and Selection
In robot manipulation, planning the motion of a robot manipulator to grasp an object is a fundamental problem.
Robotics
Self-supervised 6D Object Pose Estimation for Robot Manipulation
In this way, our system is able to continuously collect data and improve its pose estimation modules.
Robotics
The Best of Both Modes: Separately Leveraging RGB and Depth for Unseen Object Instance Segmentation
We show that our method, trained on this dataset, can produce sharp and accurate masks, outperforming state-of-the-art methods on unseen object instance segmentation.
PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking
In this work, we formulate the 6D object pose tracking problem in the Rao-Blackwellized particle filtering framework, where the 3D rotation and the 3D translation of an object are decoupled.
Object Discovery in Videos as Foreground Motion Clustering
We consider the problem of providing dense segmentation masks for object discovery in videos.
Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects
Using synthetic data generated in this manner, we introduce a one-shot deep neural network that is able to perform competitively against a state-of-the-art network trained on a combination of real and synthetic data.
Robotics
DeepIM: Deep Iterative Matching for 6D Pose Estimation
Estimating the 6D pose of objects from images is an important problem in various applications such as robot manipulation and virtual reality.
Ranked #1 on
6D Pose Estimation using RGB
on YCB-Video
Recurrent Autoregressive Networks for Online Multi-Object Tracking
The external memory explicitly stores previous inputs of each trajectory in a time window, while the internal memory learns to summarize long-term tracking history and associate detections by processing the external memory.
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
We conduct extensive experiments on our YCB-Video dataset and the OccludedLINEMOD dataset to show that PoseCNN is highly robust to occlusions, can handle symmetric objects, and provide accurate pose estimation using only color images as input.
Ranked #3 on
6D Pose Estimation using RGBD
on YCB-Video
DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks
3D scene understanding is important for robots to interact with the 3D world in a meaningful way.
Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection
In CNN-based object detection methods, region proposal becomes a bottleneck when objects exhibit significant scale variation, occlusion or truncation.
Ranked #4 on
Vehicle Pose Estimation
on KITTI Cars Hard
Learning to Track: Online Multi-Object Tracking by Decision Making
Online Multi-Object Tracking (MOT) has wide applications in time-critical video analysis scenarios, such as robot navigation and autonomous driving.
Ranked #18 on
Multiple Object Tracking
on KITTI Tracking test
Deep Metric Learning via Lifted Structured Feature Embedding
Additionally, we collected Online Products dataset: 120k images of 23k classes of online products for metric learning.
Data-Driven 3D Voxel Patterns for Object Category Recognition
Despite the great progress achieved in recognizing objects as 2D bounding boxes in images, it is still very challenging to detect occluded objects and estimate the 3D properties of multiple objects from a single image.
A Coarse-to-Fine Model for 3D Pose Estimation and Sub-category Recognition
Despite the fact that object detection, 3D pose estimation, and sub-category recognition are highly correlated tasks, they are usually addressed independently from each other because of the huge space of parameters.
