Search Results for author: Max Q. -H. Meng

Found 21 papers, 9 papers with code

Joint Sparse Representations and Coupled Dictionary Learning in Multi-Source Heterogeneous Image Pseudo-color Fusion

no code implementations15 Oct 2023 Long Bai, Shilong Yao, Kun Gao, Yanjun Huang, Ruijie Tang, Hong Yan, Max Q. -H. Meng, Hongliang Ren

Considering that Coupled Dictionary Learning (CDL) method can obtain a reasonable linear mathematical relationship between resource images, we propose a novel CDL-based Synthetic Aperture Radar (SAR) and multispectral pseudo-color fusion method.

Dictionary Learning

Discrepancy-based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images

no code implementations9 Aug 2023 Fan Bai, Xiaohan Xing, Yutian SHEN, Han Ma, Max Q. -H. Meng

Specifically, to liberate labor, we design a novel discrepancy decoder model and a CAMPUS (CAM, Pseudo-label and groUnd-truth Selection) criterion to replace the noisy CAMs with accurate model predictions and a few human labels.

Active Learning Pseudo Label

SLPT: Selective Labeling Meets Prompt Tuning on Label-Limited Lesion Segmentation

no code implementations9 Aug 2023 Fan Bai, Ke Yan, Xiaoyu Bai, Xinyu Mao, Xiaoli Yin, Jingren Zhou, Yu Shi, Le Lu, Max Q. -H. Meng

We evaluate our method on liver tumor segmentation and achieve state-of-the-art performance, outperforming traditional fine-tuning with only 6% of tunable parameters, also achieving 94% of full-data performance by labeling only 5% of the data.

Lesion Segmentation Tumor Segmentation

Deep Koopman Operator with Control for Nonlinear Systems

1 code implementation16 Feb 2022 Haojie Shi, Max Q. -H. Meng

Furthermore, most Koopman-based algorithms only consider nonlinear systems with linear control input, resulting in lousy prediction and control performance when the system is fully nonlinear with the control input.

Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework

no code implementations3 Nov 2021 Keyu Li, Yangxin Xu, Jian Wang, Dong Ni, Li Liu, Max Q. -H. Meng

Ultrasound (US) imaging is commonly used to assist in the diagnosis and interventions of spine diseases, while the standardized US acquisitions performed by manually operating the probe require substantial experience and training of sonographers.

Anatomy Decision Making +2

Human-Aware Robot Navigation via Reinforcement Learning with Hindsight Experience Replay and Curriculum Learning

no code implementations9 Oct 2021 Keyu Li, Ye Lu, Max Q. -H. Meng

In recent years, the growing demand for more intelligent service robots is pushing the development of mobile robot navigation algorithms to allow safe and efficient operation in a dense crowd.

Decision Making Reinforcement Learning (RL) +1

Automatic Recognition of Abdominal Organs in Ultrasound Images based on Deep Neural Networks and K-Nearest-Neighbor Classification

1 code implementation9 Oct 2021 Keyu Li, Yangxin Xu, Max Q. -H. Meng

In order to shorten the examination time and reduce the cognitive burden on the sonographers, we present a classification method that combines the deep learning techniques and k-Nearest-Neighbor (k-NN) classification to automatically recognize various abdominal organs in the ultrasound images in real time.

Classification Dimensionality Reduction +1

Hierarchical Policy for Non-prehensile Multi-object Rearrangement with Deep Reinforcement Learning and Monte Carlo Tree Search

1 code implementation18 Sep 2021 Fan Bai, Fei Meng, Jianbang Liu, Jiankun Wang, Max Q. -H. Meng

Non-prehensile multi-object rearrangement is a robotic task of planning feasible paths and transferring multiple objects to their predefined target poses without grasping.


Reinforcement Learning with Evolutionary Trajectory Generator: A General Approach for Quadrupedal Locomotion

1 code implementation14 Sep 2021 Haojie Shi, Bo Zhou, Hongsheng Zeng, Fan Wang, Yueqiang Dong, Jiangyong Li, Kang Wang, Hao Tian, Max Q. -H. Meng

However, due to the complex nonlinear dynamics in quadrupedal robots and reward sparsity, it is still difficult for RL to learn effective gaits from scratch, especially in challenging tasks such as walking over the balance beam.

reinforcement-learning Reinforcement Learning (RL)

Deep Learning-based Biological Anatomical Landmark Detection in Colonoscopy Videos

no code implementations6 Aug 2021 Kaiwei Che, Chengwei Ye, Yibing Yao, Nachuan Ma, Ruo Zhang, Jiankun Wang, Max Q. -H. Meng

Second, a ResNet-101 based network is used to detect three biological anatomical landmarks separately to obtain the intermediate detection results.

No Need for Interactions: Robust Model-Based Imitation Learning using Neural ODE

1 code implementation3 Apr 2021 HaoChih Lin, Baopu Li, Xin Zhou, Jiankun Wang, Max Q. -H. Meng

Interactions with either environments or expert policies during training are needed for most of the current imitation learning (IL) algorithms.

Imitation Learning

Efficient Heuristic Generation for Robot Path Planning with Recurrent Generative Model

no code implementations7 Dec 2020 Zhaoting Li, Jiankun Wang, Max Q. -H. Meng

To address this issue, we present a novel recurrent generative model (RGM) which generates efficient heuristic to reduce the search efforts of path planning algorithm.

Autonomous Removal of Perspective Distortion for Robotic Elevator Button Recognition

no code implementations26 Dec 2019 Delong Zhu, Jianbang Liu, Nachuan Ma, Zhe Min, Max Q. -H. Meng

To verify the effectiveness of the algorithm, we collect an elevator panel dataset of 50 images captured from different angles of view.

Learning to Interrupt: A Hierarchical Deep Reinforcement Learning Framework for Efficient Exploration

no code implementations30 Jul 2018 Tingguang Li, Jin Pan, Delong Zhu, Max Q. -H. Meng

Our architecture has two key components: options, represented by existing human-designed methods, can significantly speed up the training process and interruption mechanism, based on learnable termination functions, enables our system to quickly respond to the external environment.

Efficient Exploration Hierarchical Reinforcement Learning +3

Inverse Reinforcement Learning with Multi-Relational Chains for Robot-Centered Smart Home

no code implementations16 Aug 2014 Kun Li, Max Q. -H. Meng

In a robot-centered smart home, the robot observes the home states with its own sensors, and then it can change certain object states according to an operator's commands for remote operations, or imitate the operator's behaviors in the house for autonomous operations.

reinforcement-learning Reinforcement Learning (RL)

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