Search Results for author: Zhe Liu

Found 105 papers, 34 papers with code

Recovering from Privacy-Preserving Masking with Large Language Models

no code implementations12 Sep 2023 Arpita Vats, Zhe Liu, Peng Su, Debjyoti Paul, Yingyi Ma, Yutong Pang, Zeeshan Ahmed, Ozlem Kalinli

To effectively perform adaptation, textual data of users is typically stored on servers or their local devices, where downstream natural language processing (NLP) models can be directly trained using such in-domain data.

Language Modelling Privacy Preserving

Sparse Federated Training of Object Detection in the Internet of Vehicles

no code implementations7 Sep 2023 Luping Rao, Chuan Ma, Ming Ding, Yuwen Qian, Lu Zhou, Zhe Liu

However, the current object detection methods are mostly based on centralized deep training, that is, the sensitive data obtained by edge devices need to be uploaded to the server, which raises privacy concerns.

Federated Learning object-detection +1

Contextual Biasing of Named-Entities with Large Language Models

no code implementations1 Sep 2023 Chuanneng Sun, Zeeshan Ahmed, Yingyi Ma, Zhe Liu, Lucas Kabela, Yutong Pang, Ozlem Kalinli

We propose to leverage prompts for a LLM without fine tuning during rescoring which incorporate a biasing list and few-shot examples to serve as additional information when calculating the score for the hypothesis.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

OnUVS: Online Feature Decoupling Framework for High-Fidelity Ultrasound Video Synthesis

no code implementations16 Aug 2023 Han Zhou, Dong Ni, Ao Chang, Xinrui Zhou, Rusi Chen, Yanlin Chen, Lian Liu, Jiamin Liang, Yuhao Huang, Tong Han, Zhe Liu, Deng-Ping Fan, Xin Yang

Second, to better preserve the integrity and textural information of US images, we implemented a dual-decoder that decouples the content and textural features in the generator.

RLSAC: Reinforcement Learning enhanced Sample Consensus for End-to-End Robust Estimation

1 code implementation10 Aug 2023 Chang Nie, Guangming Wang, Zhe Liu, Luca Cavalli, Marc Pollefeys, Hesheng Wang

Therefore, RLSAC can avoid differentiating to learn the features and the feedback of downstream tasks for end-to-end robust estimation.


End-to-end 2D-3D Registration between Image and LiDAR Point Cloud for Vehicle Localization

no code implementations20 Jun 2023 Guangming Wang, Yu Zheng, Yanfeng Guo, Zhe Liu, Yixiang Zhu, Wolfram Burgard, Hesheng Wang

A popular approach to robot localization is based on image-to-point cloud registration, which combines illumination-invariant LiDAR-based mapping with economical image-based localization.

Image-Based Localization Image to Point Cloud Registration

G$^2$uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering

no code implementations8 Jun 2023 Hao Yu, Chuan Ma, Meng Liu, Xinwang Liu, Zhe Liu, Ming Ding

As a collaborative paradigm, Federated Learning (FL) empowers clients to engage in collective model training without exchanging their respective local data.

Anomaly Detection Clustering +2

SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model

1 code implementation4 Jun 2023 Dingyuan Zhang, Dingkang Liang, Hongcheng Yang, Zhikang Zou, Xiaoqing Ye, Zhe Liu, Xiang Bai

In the spirit of unleashing the capability of foundation models on vision tasks, the Segment Anything Model (SAM), a vision foundation model for image segmentation, has been proposed recently and presents strong zero-shot ability on many downstream 2D tasks.

3D Object Detection Image Segmentation +2

E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition

1 code implementation29 May 2023 Zhen Zhang, Mengting Hu, Shiwan Zhao, Minlie Huang, Haotian Wang, Lemao Liu, Zhirui Zhang, Zhe Liu, Bingzhe Wu

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 systems in open environments.

named-entity-recognition Named Entity Recognition +1

Multi-Modal 3D Object Detection by Box Matching

1 code implementation12 May 2023 Zhe Liu, Xiaoqing Ye, Zhikang Zou, Xinwei He, Xiao Tan, Errui Ding, Jingdong Wang, Xiang Bai

Extensive experiments on the nuScenes dataset demonstrate that our method is much more stable in dealing with challenging cases such as asynchronous sensors, misaligned sensor placement, and degenerated camera images than existing fusion methods.

3D Object Detection Autonomous Driving +1

RARE: Robust Masked Graph Autoencoder

no code implementations4 Apr 2023 Wenxuan Tu, Qing Liao, Sihang Zhou, Xin Peng, Chuan Ma, Zhe Liu, Xinwang Liu, Zhiping Cai

To address this issue, we propose a novel SGP method termed Robust mAsked gRaph autoEncoder (RARE) to improve the certainty in inferring masked data and the reliability of the self-supervision mechanism by further masking and reconstructing node samples in the high-order latent feature space.

Regularized Shallow Image Prior for Electrical Impedance Tomography

no code implementations30 Mar 2023 Zhe Liu, Zhou Chen, Qi Wang, Sheng Zhang, Yunjie Yang

The results suggest that combining the shallow image prior and the hand-crafted regularization can achieve similar performance to the Deep Image Prior (DIP) but with less architectural dependency and complexity of the neural network.

RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration

1 code implementation22 Mar 2023 Jiuming Liu, Guangming Wang, Zhe Liu, Chaokang Jiang, Marc Pollefeys, Hesheng Wang

Specifically, a projection-aware hierarchical transformer is proposed to capture long-range dependencies and filter outliers by extracting point features globally.

Point Cloud Registration

Mode-locking Theory for Long-Range Interaction in Artificial Neural Networks

no code implementations10 Mar 2023 Xiuxiu Bai, Shuaishuai Zhao, Yao Gao, Zhe Liu

We verify this theory through simulation experiments and demonstrate the mode-locking pattern in real-world scene models.

Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learning

no code implementations1 Mar 2023 Yun Li, Zhe Liu, Saurav Jha, Sally Cripps, Lina Yao

Open-World Compositional Zero-Shot Learning (OW-CZSL) aims to recognize new compositions of seen attributes and objects.

Compositional Zero-Shot Learning Knowledge Distillation

Efficient and Low Overhead Website Fingerprinting Attacks and Defenses based on TCP/IP Traffic

no code implementations27 Feb 2023 Guodong Huang, Chuan Ma, Ming Ding, Yuwen Qian, Chunpeng Ge, Liming Fang, Zhe Liu

To achieve a configurable trade-off between the defense and the network overhead, we further improve the list-based defense by a traffic splitting mechanism, which can combat the mentioned attacks as well as save a considerable amount of network overhead.

Website Fingerprinting Attacks

Auto-weighted Multi-view Clustering for Large-scale Data

1 code implementation21 Jan 2023 Xinhang Wan, Xinwang Liu, Jiyuan Liu, Siwei Wang, Yi Wen, Weixuan Liang, En Zhu, Zhe Liu, Lu Zhou

Multi-view clustering has gained broad attention owing to its capacity to exploit complementary information across multiple data views.


StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection

no code implementations4 Jan 2023 Zhe Liu, Xiaoqing Ye, Xiao Tan, Errui Ding, Xiang Bai

In this paper, we propose a cross-modal distillation method named StereoDistill to narrow the gap between the stereo and LiDAR-based approaches via distilling the stereo detectors from the superior LiDAR model at the response level, which is usually overlooked in 3D object detection distillation.

3D Object Detection object-detection

Hilbert Distillation for Cross-Dimensionality Networks

1 code implementation8 Nov 2022 Dian Qin, Haishuai Wang, Zhe Liu, Hongjia Xu, Sheng Zhou, Jiajun Bu

Since the distilled 2D networks are supervised by the curves converted from dimensionally heterogeneous 3D features, the 2D networks are given an informative view in terms of learning structural information embedded in well-trained high-dimensional representations.

Simple Primitives with Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-shot Learning

no code implementations5 Nov 2022 Zhe Liu, Yun Li, Lina Yao, Xiaojun Chang, Wei Fang, XiaoJun Wu, Yi Yang

We design Semantic Attention (SA) and generative Knowledge Disentanglement (KD) to learn the dependence of feasibility and contextuality, respectively.

Compositional Zero-Shot Learning Disentanglement

Mitigating Unintended Memorization in Language Models via Alternating Teaching

no code implementations13 Oct 2022 Zhe Liu, Xuedong Zhang, Fuchun Peng

Recent research has shown that language models have a tendency to memorize rare or unique sequences in the training corpora which can thus leak sensitive attributes of user data.

Memorization Privacy Preserving

Group Personalized Federated Learning

no code implementations4 Oct 2022 Zhe Liu, Yue Hui, Fuchun Peng

Federated learning (FL) can help promote data privacy by training a shared model in a de-centralized manner on the physical devices of clients.

Personalized Federated Learning

Modeling Dependent Structure for Utterances in ASR Evaluation

no code implementations7 Sep 2022 Zhe Liu, Fuchun Peng

In this paper, we present graphical lasso based methods to explicitly model such dependency and estimate uncorrelated blocks of utterances in a rigorous way, after which blockwise bootstrap is applied on top of the inferred blocks.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

PromptAttack: Prompt-based Attack for Language Models via Gradient Search

no code implementations5 Sep 2022 Yundi Shi, Piji Li, Changchun Yin, Zhaoyang Han, Lu Zhou, Zhe Liu

Therefore, in this paper, we propose a malicious prompt template construction method (\textbf{PromptAttack}) to probe the security performance of PLMs.

See What the Robot Can't See: Learning Cooperative Perception for Visual Navigation

no code implementations1 Aug 2022 Jan Blumenkamp, QingBiao Li, Binyu Wang, Zhe Liu, Amanda Prorok

We consider the problem of navigating a mobile robot towards a target in an unknown environment that is endowed with visual sensors, where neither the robot nor the sensors have access to global positioning information and only use first-person-view images.

Imitation Learning Navigate +1

What Matters for 3D Scene Flow Network

1 code implementation19 Jul 2022 Guangming Wang, Yunzhe Hu, Zhe Liu, Yiyang Zhou, Masayoshi Tomizuka, Wei Zhan, Hesheng Wang

Our proposed model surpasses all existing methods by at least 38. 2% on FlyingThings3D dataset and 24. 7% on KITTI Scene Flow dataset for EPE3D metric.

Scene Flow Estimation

SparseDet: Towards End-to-End 3D Object Detection

no code implementations2 Jun 2022 Jianhong Han, Zhaoyi Wan, Zhe Liu, Jie Feng, Bingfeng Zhou

We believe this end-to-end paradigm of SparseDet will inspire new thinking on the sparsity of 3D object detection.

3D Object Detection object-detection

Emergence of Double-slit Interference by Representing Visual Space in Artificial Neural Networks

no code implementations20 May 2022 Xiuxiu Bai, Zhe Liu, Yao Gao, Bin Liu, Yongqiang Hao

Artificial neural networks have realized incredible successes at image recognition, but the underlying mechanism of visual space representation remains a huge mystery.

Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus

no code implementations3 May 2022 Yun Li, Zhe Liu, Lina Yao, Molly Lucas, Jessica J. M. Monaghan, Yu Zhang

With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses.

BIG-bench Machine Learning EEG +2

Disentangled and Side-aware Unsupervised Domain Adaptation for Cross-dataset Subjective Tinnitus Diagnosis

no code implementations3 May 2022 Yun Li, Zhe Liu, Lina Yao, Jessica J. M. Monaghan, David Mcalpine

The side-aware unsupervised domain adaptation module adapts the class-irrelevant information as domain variance to a new dataset and excludes the variance to obtain the class-distill features for the new dataset classification.

EEG Electroencephalogram (EEG) +1

FedSynth: Gradient Compression via Synthetic Data in Federated Learning

1 code implementation4 Apr 2022 Shengyuan Hu, Jack Goetz, Kshitiz Malik, Hongyuan Zhan, Zhe Liu, Yue Liu

Model compression is important in federated learning (FL) with large models to reduce communication cost.

Federated Learning Model Compression

Neural-FST Class Language Model for End-to-End Speech Recognition

no code implementations28 Jan 2022 Antoine Bruguier, Duc Le, Rohit Prabhavalkar, Dangna Li, Zhe Liu, Bo wang, Eun Chang, Fuchun Peng, Ozlem Kalinli, Michael L. Seltzer

We propose Neural-FST Class Language Model (NFCLM) for end-to-end speech recognition, a novel method that combines neural network language models (NNLMs) and finite state transducers (FSTs) in a mathematically consistent framework.

Language Modelling speech-recognition +1

SAR-GPA: SAR Generation Perturbation Algorithm

no code implementations ACM 2022 Zhe Liu, Weijie Xia, Yongzhen Lei

Finally, We design a series of simulation and experiment to verify the effectiveness of the adversarial examples and also the modulation sequences.

Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning

no code implementations6 Jan 2022 Yun Li, Zhe Liu, Xiaojun Chang, Julian McAuley, Lina Yao

We further propose a differentiable dataset-level balance and update the weights in a linear annealing schedule to simulate network pruning and thus obtain the optimal structure for BSNet with dataset-level balance achieved.

Meta-Learning Network Pruning +1

EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection

1 code implementation21 Dec 2021 Zhe Liu, Tengteng Huang, Bingling Li, Xiwu Chen, Xi Wang, Xiang Bai

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 modalities naturally possess strong complementarity.

3D Object Detection object-detection

PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysis

1 code implementation9 Dec 2021 Silin Cheng, Xiwu Chen, Xinwei He, Zhe Liu, Xiang Bai

Learning intra-region contexts and inter-region relations are two effective strategies to strengthen feature representations for point cloud analysis.

3D Point Cloud Classification Keypoint Estimation

Rethink, Revisit, Revise: A Spiral Reinforced Self-Revised Network for Zero-Shot Learning

no code implementations1 Dec 2021 Zhe Liu, Yun Li, Lina Yao, Julian McAuley, Sam Dixon

Our framework outperforms state-of-the-art algorithms on four benchmark datasets in both zero-shot and generalized zero-shot settings, which demonstrates the effectiveness of spiral learning in learning generalizable and complex correlations.

Zero-Shot Learning

Efficient 3D Deep LiDAR Odometry

1 code implementation3 Nov 2021 Guangming Wang, Xinrui Wu, Shuyang Jiang, Zhe Liu, Hesheng Wang

An efficient 3D point cloud learning architecture, named EfficientLO-Net, for LiDAR odometry is first proposed in this paper.

Pose Estimation

An Entropy-guided Reinforced Partial Convolutional Network for Zero-Shot Learning

no code implementations3 Nov 2021 Yun Li, Zhe Liu, Lina Yao, Xianzhi Wang, Julian McAuley, Xiaojun Chang

Zero-Shot Learning (ZSL) aims to transfer learned knowledge from observed classes to unseen classes via semantic correlations.

Generalized Zero-Shot Learning

Machine Learning with Knowledge Constraints for Process Optimization of Open-Air Perovskite Solar Cell Manufacturing

1 code implementation1 Oct 2021 Zhe Liu, Nicholas Rolston, Austin C. Flick, Thomas W. Colburn, Zekun Ren, Reinhold H. Dauskardt, Tonio Buonassisi

With a limited experimental budget of screening 100 process conditions, we demonstrated an efficiency improvement to 18. 5% as the best-in-our-lab device fabricated by RSPP, and we also experimentally found 10 unique process conditions to produce the top-performing devices of more than 17% efficiency, which is 5 times higher rate of success than the control experiments with pseudo-random Latin hypercube sampling.

Bayesian Optimization Benchmarking +1

Private Language Model Adaptation for Speech Recognition

no code implementations28 Sep 2021 Zhe Liu, Ke Li, Shreyan Bakshi, Fuchun Peng

Speech model adaptation is crucial to handle the discrepancy between server-side proxy training data and actual data received on local devices of users.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Model-Based Approach for Measuring the Fairness in ASR

no code implementations19 Sep 2021 Zhe Liu, Irina-Elena Veliche, Fuchun Peng

The issue of fairness arises when the automatic speech recognition (ASR) systems do not perform equally well for all subgroups of the population.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Efficient Medical Image Segmentation Based on Knowledge Distillation

1 code implementation23 Aug 2021 Dian Qin, Jiajun Bu, Zhe Liu, Xin Shen, Sheng Zhou, Jingjun Gu, Zhijua Wang, Lei Wu, Huifen Dai

To deal with this problem, we propose an efficient architecture by distilling knowledge from well-trained medical image segmentation networks to train another lightweight network.

Image Segmentation Knowledge Distillation +2

CONet: Channel Optimization for Convolutional Neural Networks

1 code implementation15 Aug 2021 Mahdi S. Hosseini, Jia Shu Zhang, Zhe Liu, Andre Fu, Jingxuan Su, Mathieu Tuli, Sepehr Hosseini, Arsh Kadakia, Haoran Wang, Konstantinos N. Plataniotis

To solve this, we introduce an efficient dynamic scaling algorithm -- CONet -- that automatically optimizes channel sizes across network layers for a given CNN.

Neural Architecture Search

The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts

no code implementations26 Jul 2021 Amanda Prorok, Jan Blumenkamp, QingBiao Li, Ryan Kortvelesy, Zhe Liu, Ethan Stump

Many multi-robot planning problems are burdened by the curse of dimensionality, which compounds the difficulty of applying solutions to large-scale problem instances.

Motion Projection Consistency Based 3D Human Pose Estimation with Virtual Bones from Monocular Videos

no code implementations28 Jun 2021 Guangming Wang, Honghao Zeng, Ziliang Wang, Zhe Liu, Hesheng Wang

Ablation studies demonstrate the effectiveness of the proposed inter-frame projection consistency constraints and intra-frame loop constraints.

3D Human Pose Estimation

Impedance-optical Dual-modal Cell Culture Imaging with Learning-based Information Fusion

no code implementations15 Jun 2021 Zhe Liu, Pierre Bagnaninchi, Yunjie Yang

While Electrical Impedance Tomography (EIT) has found many biomedicine applications, a better resolution is needed to provide quantitative analysis for tissue engineering and regenerative medicine.

Cultural Vocal Bursts Intensity Prediction

When and Why does a Model Fail? A Human-in-the-loop Error Detection Framework for Sentiment Analysis

no code implementations2 Jun 2021 Zhe Liu, Yufan Guo, Jalal Mahmud

Although deep neural networks have been widely employed and proven effective in sentiment analysis tasks, it remains challenging for model developers to assess their models for erroneous predictions that might exist prior to deployment.

Sentiment Analysis

When and Why a Model Fails? A Human-in-the-loop Error Detection Framework for Sentiment Analysis

no code implementations NAACL 2021 Zhe Liu, Yufan Guo, Jalal Mahmud

Although deep neural networks have been widely employed and proven effective in sentiment analysis tasks, it remains challenging for model developers to assess their models for erroneous predictions that might exist prior to deployment.

Sentiment Analysis

THG: Transformer with Hyperbolic Geometry

no code implementations1 Jun 2021 Zhe Liu, Yibin Xu

In this work, we propose a novel Transformer with Hyperbolic Geometry (THG) model, which take the advantage of both Euclidean space and Hyperbolic space.

Machine Reading Comprehension

Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains

1 code implementation23 May 2021 Qiaohao Liang, Aldair E. Gongora, Zekun Ren, Armi Tiihonen, Zhe Liu, Shijing Sun, James R. Deneault, Daniil Bash, Flore Mekki-Berrada, Saif A. Khan, Kedar Hippalgaonkar, Benji Maruyama, Keith A. Brown, John Fisher III, Tonio Buonassisi

In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems.

Active Learning Benchmarking +2

Accountable Error Characterization

no code implementations NAACL (TrustNLP) 2021 Amita Misra, Zhe Liu, Jalal Mahmud

Customers of machine learning systems demand accountability from the companies employing these algorithms for various prediction tasks.

Sentiment Analysis

Attribute-Modulated Generative Meta Learning for Zero-Shot Classification

no code implementations22 Apr 2021 Yun Li, Zhe Liu, Lina Yao, Xiaojun Chang

The promising strategies for ZSL are to synthesize visual features of unseen classes conditioned on semantic side information and to incorporate meta-learning to eliminate the model's inherent bias towards seen classes.

Classification General Classification +5

Task Aligned Generative Meta-learning for Zero-shot Learning

no code implementations3 Mar 2021 Zhe Liu, Yun Li, Lina Yao, Xianzhi Wang, Guodong Long

Zero-shot learning (ZSL) refers to the problem of learning to classify instances from the novel classes (unseen) that are absent in the training set (seen).

Generalized Zero-Shot Learning Meta-Learning

GAKP: GRU Association and Kalman Prediction for Multiple Object Tracking

no code implementations28 Dec 2020 Zhen Li, Sunzeng Cai, Xiaoyi Wang, Zhe Liu, Nian Xue

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.

Multiple Object Tracking

A Registration-aided Domain Adaptation Network for 3D Point Cloud Based Place Recognition

1 code implementation9 Dec 2020 Zhijian Qiao, Hanjiang Hu, Weiang Shi, Siyuan Chen, Zhe Liu, Hesheng Wang

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 to changing environments with drastic daytime and weather variance.

3D Place Recognition Autonomous Driving +2

ProMask: Probability Mask for Skeleton Detection

no code implementations5 Dec 2020 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.

PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization

1 code implementation CVPR 2021 Guangming Wang, Xinrui Wu, Zhe Liu, Hesheng Wang

A novel 3D point cloud learning model for deep LiDAR odometry, named PWCLO-Net, using hierarchical embedding mask optimization is proposed in this paper.

Pose Estimation

Federated Marginal Personalization for ASR Rescoring

no code implementations1 Dec 2020 Zhe Liu, Fuchun Peng

Our presented approach can overcome the limitations of federated fine-tuning and efficiently learn personalized NNLMs on devices.

Federated Learning speech-recognition +1

Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction

1 code implementation COLING 2020 Huiwei Zhou, Yibin Xu, Weihong Yao, Zhe Liu, Chengkun Lang, Haibin Jiang

In this paper, we propose Global Context-enhanced Graph Convolutional Networks (GCGCN), a novel model which is composed of entities as nodes and context of entity pairs as edges between nodes to capture rich global context information of entities in a document.

Document-level Relation Extraction

End-to-End 3D Point Cloud Learning for Registration Task Using Virtual Correspondences

1 code implementation30 Nov 2020 Zhijian Qiao, Huanshu Wei, Zhe Liu, Chuanzhe Suo, Hesheng Wang

3D Point cloud registration is still a very challenging topic due to the difficulty in finding the rigid transformation between two point clouds with partial correspondences, and it's even harder in the absence of any initial estimation information.

Point Cloud Registration

3D Invisible Cloak

no code implementations27 Nov 2020 Mingfu Xue, Can He, Zhiyu Wu, Jian Wang, Zhe Liu, Weiqiang Liu

on person stealth attacks, and propose 3D transformations to generate 3D invisible cloak.

Spherical Interpolated Convolutional Network with Distance-Feature Density for 3D Semantic Segmentation of Point Clouds

no code implementations27 Nov 2020 Guangming Wang, Yehui Yang, Huixin Zhang, Zhe Liu, Hesheng Wang

In this paper, a spherical interpolated convolution operator is proposed to replace the traditional grid-shaped 3D convolution operator.

3D Semantic Segmentation

Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning

1 code implementation26 Nov 2020 QingBiao Li, Weizhe Lin, Zhe Liu, Amanda Prorok

Our Message-Aware Graph Attention neTwork (MAGAT) is based on a key-query-like mechanism that determines the relative importance of features in the messages received from various neighboring robots.

Graph Attention

Hierarchical Attention Learning of Scene Flow in 3D Point Clouds

no code implementations12 Oct 2020 Guangming Wang, Xinrui Wu, Zhe Liu, Hesheng Wang

In this paper, a novel hierarchical neural network with double attention is proposed for learning the correlation of point features in adjacent frames and refining scene flow from coarse to fine layer by layer.

Autonomous Driving Optical Flow Estimation +1

DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based Localization

1 code implementation1 Oct 2020 Hanjiang Hu, Zhijian Qiao, Ming Cheng, Zhe Liu, Hesheng Wang

Long-Term visual localization under changing environments is a challenging problem in autonomous driving and mobile robotics due to season, illumination variance, etc.

Autonomous Driving Domain Adaptation +5

EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection

1 code implementation ECCV 2020 Tengteng Huang, Zhe Liu, Xiwu Chen, Xiang Bai

In this paper, we aim at addressing two critical issues in the 3D detection task, including the exploitation of multiple sensors~(namely LiDAR point cloud and camera image), as well as the inconsistency between the localization and classification confidence.

3D Object Detection General Classification +1

Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding

no code implementations14 Jul 2020 Zhe Liu, Xianzhi Wang, Lina Yao, Jake An, Lei Bai, Ee-Peng Lim

We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers and to predict the top-$N$ purchase destinations of a consumer.

Spectrum-Guided Adversarial Disparity Learning

1 code implementation14 Jul 2020 Zhe Liu, Lina Yao, Lei Bai, Xianzhi Wang, Can Wang

It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between subject-specific variation for each activity class.

Activity Recognition Denoising

Agglomerative Neural Networks for Multi-view Clustering

no code implementations12 May 2020 Zhe Liu, Yun Li, Lina Yao, Xianzhi Wang, Feiping Nie

Conventional multi-view clustering methods seek for a view consensus through minimizing the pairwise discrepancy between the consensus and subviews.


Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learning

1 code implementation11 May 2020 Binyu Wang, Zhe Liu, Qing-Biao Li, Amanda Prorok

Path planning for mobile robots in large dynamic environments is a challenging problem, as the robots are required to efficiently reach their given goals while simultaneously avoiding potential conflicts with other robots or dynamic objects.

reinforcement-learning Reinforcement Learning (RL)

Are You A Risk Taker? Adversarial Learning of Asymmetric Cross-Domain Alignment for Risk Tolerance Prediction

no code implementations18 Apr 2020 Zhe Liu, Lina Yao, Xianzhi Wang, Lei Bai, Jake An

Most current studies on survey analysis and risk tolerance modelling lack professional knowledge and domain-specific models.

Representation Learning

Statistical Testing on ASR Performance via Blockwise Bootstrap

no code implementations19 Dec 2019 Zhe Liu, Fuchun Peng

A common question being raised in automatic speech recognition (ASR) evaluations is how reliable is an observed word error rate (WER) improvement comparing two ASR systems, where statistical hypothesis testing and confidence interval (CI) can be utilized to tell whether this improvement is real or only due to random chance.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

TANet: Robust 3D Object Detection from Point Clouds with Triple Attention

1 code implementation11 Dec 2019 Zhe Liu, Xin Zhao, Tengteng Huang, Ruolan Hu, Yu Zhou, Xiang Bai

In this paper, we focus on exploring the robustness of the 3D object detection in point clouds, which has been rarely discussed in existing approaches.

object-detection Robust 3D Object Detection

Nanoconfined, dynamic electrolyte gating and memory effects in multilayered graphene-based membranes

no code implementations29 Nov 2019 Jing Xiao, Hualin Zhan, Zaiquan Xu, Xiao Wang, Ke Zhang, Zhiyuan Xiong, George P. Simon, Zhe Liu, Dan Li

Multilayered graphene-based nanoporous membranes with electrolyte incorporated between individual sheets is a unique nano-heterostructure system in which nanoconfined electrons in graphene and ions confined in between sheets are intimately coupled throughout the entire membrane.

Mesoscale and Nanoscale Physics Materials Science Soft Condensed Matter Applied Physics Chemical Physics

Improving N-gram Language Models with Pre-trained Deep Transformer

no code implementations22 Nov 2019 Yiren Wang, Hongzhao Huang, Zhe Liu, Yutong Pang, Yongqiang Wang, ChengXiang Zhai, Fuchun Peng

Although n-gram language models (LMs) have been outperformed by the state-of-the-art neural LMs, they are still widely used in speech recognition due to its high efficiency in inference.

Data Augmentation speech-recognition +2

Teacher-Student Learning Paradigm for Tri-training: An Efficient Method for Unlabeled Data Exploitation

no code implementations25 Sep 2019 Yash Bhalgat, Zhe Liu, Pritam Gundecha, Jalal Mahmud, Amita Misra

Given that labeled data is expensive to obtain in real-world scenarios, many semi-supervised algorithms have explored the task of exploitation of unlabeled data.

Sentiment Analysis

Retrieval-based Localization Based on Domain-invariant Feature Learning under Changing Environments

1 code implementation23 Sep 2019 Hanjiang Hu, Hesheng Wang, Zhe Liu, Chenguang Yang, Weidong Chen, Le Xie

To retrieve a target image from the database, the query image is first encoded using the encoder belonging to the query domain to obtain a domain-invariant feature vector.

Autonomous Driving Retrieval +2

Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection

1 code implementation18 Sep 2019 Xiang Zhang, Lina Yao, Manqing Dong, Zhe Liu, Yu Zhang, Yong Li

Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure.

EEG Electroencephalogram (EEG) +3

DUT-BIM at MEDIQA 2019: Utilizing Transformer Network and Medical Domain-Specific Contextualized Representations for Question Answering

no code implementations WS 2019 Huiwei Zhou, Bizun Lei, Zhe Liu, Zhuang Liu

BioNLP 2019 proposes Question Answering (QA) task, which encourages the use of text mining technology to automatically judge whether a search result is an answer to the medical question.

Question Answering

Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks

2 code implementations npj Computational Materials 2019 Felipe Oviedo, Zekun Ren, Shijing Sun, Charles Settens, Zhe Liu, Noor Titan Putri Hartono, Savitha Ramasamy, Brian L. DeCost, Siyu I. P. Tian, Giuseppe Romano, Aaron Gilad Kusne, Tonio Buonassisi

We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data.

BIG-bench Machine Learning Data Augmentation +6

3D Object Detection Using Scale Invariant and Feature Reweighting Networks

no code implementations8 Jan 2019 Xin Zhao, Zhe Liu, Ruolan Hu, Kaiqi Huang

On the other hand, our network obtains the useful features and suppresses the features with less information by a SENet module.

3D Object Detection object-detection

LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis

2 code implementations ICCV 2019 Zhe Liu, Shunbo Zhou, Chuanzhe Suo, Yingtian Liu, Peng Yin, Hesheng Wang, Yun-hui Liu

Point cloud based place recognition is still an open issue due to the difficulty in extracting local features from the raw 3D point cloud and generating the global descriptor, and it's even harder in the large-scale dynamic environments.

Point Cloud Retrieval Retrieval +1

Characterizing machine learning process: A maturity framework

no code implementations12 Nov 2018 Rama Akkiraju, Vibha Sinha, Anbang Xu, Jalal Mahmud, Pritam Gundecha, Zhe Liu, Xiaotong Liu, John Schumacher

For example, existing machine learning processes cannot address how to define business use cases for an AI application, how to convert business requirements from offering managers into data requirements for data scientists, and how to continuously improve AI applications in term of accuracy and fairness, and how to customize general purpose machine learning models with industry, domain, and use case specific data to make them more accurate for specific situations etc.

BIG-bench Machine Learning Fairness +1

Fostering User Engagement: Rhetorical Devices for Applause Generation Learnt from TED Talks

no code implementations17 Mar 2017 Zhe Liu, Anbang Xu, Mengdi Zhang, Jalal Mahmud, Vibha Sinha

One problem that every presenter faces when delivering a public discourse is how to hold the listeners' attentions or to keep them involved.


See the Near Future: A Short-Term Predictive Methodology to Traffic Load in ITS

no code implementations8 Jan 2017 Xun Zhou, Changle Li, Zhe Liu, Tom H. Luan, Zhifang Miao, Lina Zhu, Lei Xiong

Based on the Gaussian distribution of traffic flow, a hybrid model with a Bayesian learning algorithm is developed which can effectively expand the application scenarios of SARIMA.

Scheduling Time Series +1

Learning Nonparametric Forest Graphical Models with Prior Information

no code implementations12 Nov 2015 Yuancheng Zhu, Zhe Liu, Siqi Sun

We present a framework for incorporating prior information into nonparametric estimation of graphical models.

Density Estimation

Graphical Exponential Screening

no code implementations9 Mar 2015 Zhe Liu

In high dimensions we propose and analyze an aggregation estimator of the precision matrix for Gaussian graphical models.

Blossom Tree Graphical Models

no code implementations NeurIPS 2014 Zhe Liu, John Lafferty

We combine the ideas behind trees and Gaussian graphical models to form a new nonparametric family of graphical models.

An Aggregation Method for Sparse Logistic Regression

no code implementations25 Oct 2014 Zhe Liu

We also analyze a published genome-wide case-control dataset to further evaluate the usefulness of the aggregation method in multilocus association mapping.

General Classification regression

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