Search Results for author: Yu Xiang

Found 38 papers, 18 papers with code

Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network

no code implementations30 Jul 2022 Zhen Xing, Yijiang Chen, Zhixin Ling, Xiangdong Zhou, Yu Xiang

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.

3D Reconstruction Contrastive Learning +2

FewSOL: A Dataset for Few-Shot Object Learning in Robotic Environments

2 code implementations6 Jul 2022 Jishnu Jaykumar P, Yu-Wei Chao, Yu Xiang

We introduce the Few-Shot Object Learning (FewSOL) dataset for object recognition with a few images per object.

Classification Few-Shot Learning +3

A Manifold-based Airfoil Geometric-feature Extraction and Discrepant Data Fusion Learning Method

no code implementations23 Jun 2022 Yu Xiang, Guangbo Zhang, Liwei Hu, Jun Zhang, Wenyong Wang

Geometrical shape of airfoils, together with the corresponding flight conditions, are crucial factors for aerodynamic performances prediction.

Multi-Task Learning

An Invariant Matching Property for Distribution Generalization under Intervened Response

no code implementations18 May 2022 Kang Du, Yu Xiang

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

no code implementations23 Mar 2022 Yu Xiang, Yu Huang, Haodong Xu, Guangbo Zhang, Wenyong Wang

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

no code implementations6 Mar 2022 Mehrdad Pournaderi, Yu Xiang

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.

Variable Selection

iCaps: Iterative Category-level Object Pose and Shape Estimation

1 code implementation31 Dec 2021 Xinke Deng, Junyi Geng, Timothy Bretl, Yu Xiang, Dieter Fox

The auto-encoder can be used in a particle filter framework to estimate and track 6D poses of objects in a category.

Differentially Private Variable Selection via the Knockoff Filter

no code implementations12 Sep 2021 Mehrdad Pournaderi, Yu Xiang

The knockoff filter, recently developed by Barber and Candes, is an effective procedure to perform variable selection with a controlled false discovery rate (FDR).

Variable Selection

A Subsampling-Based Method for Causal Discovery on Discrete Data

no code implementations31 Aug 2021 Austin Goddard, Yu Xiang

Inferring causal directions on discrete and categorical data is an important yet challenging problem.

Causal Discovery

RICE: Refining Instance Masks in Cluttered Environments with Graph Neural Networks

1 code implementation29 Jun 2021 Christopher Xie, Arsalan Mousavian, Yu Xiang, Dieter Fox

We postulate that a network architecture that encodes relations between objects at a high-level can be beneficial.

RGB-D Local Implicit Function for Depth Completion of Transparent Objects

2 code implementations CVPR 2021 Luyang Zhu, Arsalan Mousavian, Yu Xiang, Hammad Mazhar, Jozef van Eenbergen, Shoubhik Debnath, Dieter Fox

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.

Depth Completion Depth Estimation +1

Deterministic distribution of multipartite entanglement and steering in a quantum network by separable states

no code implementations5 Jan 2021 Meihong Wang, Yu Xiang, Haijun Kang, Dongmei Han, Yang Liu, Qiongyi He, Qihuang Gong, Xiaolong Su, Kunchi Peng

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

no code implementations23 Dec 2020 Kang Du, Yu Xiang

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.

Causal Discovery Causal Identification +2

Aerodynamic Data Predictions Based on Multi-task Learning

no code implementations15 Oct 2020 Liwei Hu, Yu Xiang, Jun Zhan, Zifang Shi, Wenzheng Wang

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.

Multi-Task Learning Natural Language Processing

Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds

1 code implementation2 Oct 2020 Lirui Wang, Yu Xiang, Wei Yang, Arsalan Mousavian, Dieter Fox

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.

Imitation Learning Motion Planning +1

Information Laundering for Model Privacy

no code implementations ICLR 2021 Xinran Wang, Yu Xiang, Jun Gao, Jie Ding

In this work, we propose information laundering, a novel framework for enhancing model privacy.

Flow Field Reconstructions with GANs based on Radial Basis Functions

no code implementations11 Aug 2020 Liwei Hu, Wenyong Wang, Yu Xiang, Jun Zhang

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

1 code implementation30 Jul 2020 Yu Xiang, Christopher Xie, Arsalan Mousavian, Dieter Fox

In this work, we propose a new method for unseen object instance segmentation by learning RGB-D feature embeddings from synthetic data.

Metric Learning Semantic Segmentation +1

Manipulation Trajectory Optimization with Online Grasp Synthesis and Selection

1 code implementation22 Nov 2019 Lirui Wang, Yu Xiang, Dieter Fox

In robot manipulation, planning the motion of a robot manipulator to grasp an object is a fundamental problem.


Self-supervised 6D Object Pose Estimation for Robot Manipulation

3 code implementations23 Sep 2019 Xinke Deng, Yu Xiang, Arsalan Mousavian, Clemens Eppner, Timothy Bretl, Dieter Fox

In this way, our system is able to continuously collect data and improve its pose estimation modules.


The Best of Both Modes: Separately Leveraging RGB and Depth for Unseen Object Instance Segmentation

no code implementations30 Jul 2019 Christopher Xie, Yu Xiang, Arsalan Mousavian, Dieter Fox

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.

Semantic Segmentation Unseen Object Instance Segmentation

PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking

1 code implementation22 May 2019 Xinke Deng, Arsalan Mousavian, Yu Xiang, Fei Xia, Timothy Bretl, Dieter Fox

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.

6D Pose Estimation 6D Pose Estimation using RGB +2

Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects

7 code implementations27 Sep 2018 Jonathan Tremblay, Thang To, Balakumar Sundaralingam, Yu Xiang, Dieter Fox, Stan Birchfield

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.


DeepIM: Deep Iterative Matching for 6D Pose Estimation

1 code implementation ECCV 2018 Yi Li, Gu Wang, Xiangyang Ji, Yu Xiang, Dieter Fox

Estimating the 6D pose of objects from images is an important problem in various applications such as robot manipulation and virtual reality.

6D Pose Estimation 6D Pose Estimation using RGB

Recurrent Autoregressive Networks for Online Multi-Object Tracking

no code implementations7 Nov 2017 Kuan Fang, Yu Xiang, Xiaocheng Li, Silvio Savarese

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.

Multi-Object Tracking Online Multi-Object Tracking

PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes

10 code implementations1 Nov 2017 Yu Xiang, Tanner Schmidt, Venkatraman Narayanan, Dieter Fox

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.

6D Pose Estimation 6D Pose Estimation using RGB +1

DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks

1 code implementation9 Mar 2017 Yu Xiang, Dieter Fox

3D scene understanding is important for robots to interact with the 3D world in a meaningful way.

Scene Understanding

Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection

1 code implementation16 Apr 2016 Yu Xiang, Wongun Choi, Yuanqing Lin, Silvio Savarese

In CNN-based object detection methods, region proposal becomes a bottleneck when objects exhibit significant scale variation, occlusion or truncation.

2D object detection General Classification +4

Learning to Track: Online Multi-Object Tracking by Decision Making

no code implementations ICCV 2015 Yu Xiang, Alexandre Alahi, Silvio Savarese

Online Multi-Object Tracking (MOT) has wide applications in time-critical video analysis scenarios, such as robot navigation and autonomous driving.

Autonomous Driving Decision Making +5

Deep Metric Learning via Lifted Structured Feature Embedding

3 code implementations CVPR 2016 Hyun Oh Song, Yu Xiang, Stefanie Jegelka, Silvio Savarese

Additionally, we collected Online Products dataset: 120k images of 23k classes of online products for metric learning.

Metric Learning Structured Prediction

Data-Driven 3D Voxel Patterns for Object Category Recognition

no code implementations CVPR 2015 Yu Xiang, Wongun Choi, Yuanqing Lin, Silvio Savarese

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.

Object Recognition Pose Estimation

A Coarse-to-Fine Model for 3D Pose Estimation and Sub-category Recognition

no code implementations CVPR 2015 Roozbeh Mottaghi, Yu Xiang, Silvio Savarese

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.

3D Pose Estimation object-detection +1

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