Search Results for author: Zhong Liu

Found 25 papers, 7 papers with code

Transmit Beampattern Synthesis for Active RIS-Aided MIMO Radar via Waveform and Beamforming Optimization

no code implementations7 Oct 2024 Shengyao Chen, Minghui He, Longyao Ran, Hongtao Li, Feng Xi, Sirui Tian, Zhong Liu

We aim to minimize the integrated sidelobe-to-mainlobe ratio (ISMR) of beampattern by the codesign of waveform and ARIS reflection coefficients.

Conversational Crowdsensing: A Parallel Intelligence Powered Novel Sensing Approach

no code implementations4 Feb 2024 Zhengqiu Zhu, Yong Zhao, Bin Chen, Sihang Qiu, Kai Xu, Quanjun Yin, Jincai Huang, Zhong Liu, Fei-Yue Wang

The transition from CPS-based Industry 4. 0 to CPSS-based Industry 5. 0 brings new requirements and opportunities to current sensing approaches, especially in light of recent progress in Chatbots and Large Language Models (LLMs).

Scheduling

Reconfigurable Intelligent Surface-Enabled Array Radar for Interference Mitigation

no code implementations20 Jan 2024 Shengyao Chen, Qi Feng, Longyao Ran, Feng Xi, Zhong Liu

To maximize the output signal-to-interference-plus-noise ratio (SINR) of receive array, we formulate the codesign of transmit beamforming and RIS-assisted receive beamforming into a nonconvex constrained fractional programming problem, and then propose an alternating minimization-based algorithm to jointly optimize the transmitor beamfmer, receive beamformer and RIS reflection coefficients.

IEBins: Iterative Elastic Bins for Monocular Depth Estimation

1 code implementation NeurIPS 2023 Shuwei Shao, Zhongcai Pei, Xingming Wu, Zhong Liu, Weihai Chen, Zhengguo Li

To alleviate the possible error accumulation during the iterative process, we utilize a novel elastic target bin to replace the original target bin, the width of which is adjusted elastically based on the depth uncertainty.

Monocular Depth Estimation regression

The Expressive Power of Graph Neural Networks: A Survey

no code implementations16 Aug 2023 Bingxu Zhang, Changjun Fan, Shixuan Liu, Kuihua Huang, Xiang Zhao, Jincai Huang, Zhong Liu

Graph neural networks (GNNs) are effective machine learning models for many graph-related applications.

Subgraph Counting Survey

Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks

no code implementations8 Jul 2023 Shixuan Liu, Changjun Fan, Kewei Cheng, Yunfei Wang, Peng Cui, Yizhou Sun, Zhong Liu

Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges.

Relation

Online Unsupervised Video Object Segmentation via Contrastive Motion Clustering

2 code implementations21 Jun 2023 Lin Xi, Weihai Chen, Xingming Wu, Zhong Liu, Zhengguo Li

Online unsupervised video object segmentation (UVOS) uses the previous frames as its input to automatically separate the primary object(s) from a streaming video without using any further manual annotation.

Clustering Contrastive Learning +6

Self-Supervised Monocular Depth Estimation with Self-Reference Distillation and Disparity Offset Refinement

1 code implementation20 Feb 2023 Zhong Liu, Ran Li, Shuwei Shao, Xingming Wu, Weihai Chen

In this work, we propose two novel ideas to improve self-supervised monocular depth estimation: 1) self-reference distillation and 2) disparity offset refinement.

Monocular Depth Estimation

SMUDLP: Self-Teaching Multi-Frame Unsupervised Endoscopic Depth Estimation with Learnable Patchmatch

no code implementations30 May 2022 Shuwei Shao, Zhongcai Pei, Weihai Chen, Xingming Wu, Zhong Liu, Zhengguo Li

Unsupervised monocular trained depth estimation models make use of adjacent frames as a supervisory signal during the training phase.

Depth Estimation

Multi-ship cooperative air defense model based on queuing theory

no code implementations13 May 2022 Zhongyao Ma, Keyu Wu, Zhong Liu

Finally, through simulation experiments in typical scenarios, this paper studies and compares the air defense capabilities of the system in two different modes with and without coordination, and verifies the superiority of the multi-ship cooperative air defense model in reducing the probability of missile penetration; Further, the ability changes of the defense system under different parameters such as missile speed, speed, angle, ship interception rate, range, and number of fire units are studied, and the weak points of the defense formation, defense range settings, and interception settings are obtained.

Implicit Motion-Compensated Network for Unsupervised Video Object Segmentation

1 code implementation6 Apr 2022 Lin Xi, Weihai Chen, Xingming Wu, Zhong Liu, Zhengguo Li

Unsupervised video object segmentation (UVOS) aims at automatically separating the primary foreground object(s) from the background in a video sequence.

Motion Compensation Semantic Segmentation +2

Attentional Feature Refinement and Alignment Network for Aircraft Detection in SAR Imagery

no code implementations18 Jan 2022 Yan Zhao, Lingjun Zhao, Zhong Liu, Dewen Hu, Gangyao Kuang, Li Liu

Aircraft detection in Synthetic Aperture Radar (SAR) imagery is a challenging task in SAR Automatic Target Recognition (SAR ATR) areas due to aircraft's extremely discrete appearance, obvious intraclass variation, small size and serious background's interference.

DSRGAN: Detail Prior-Assisted Perceptual Single Image Super-Resolution via Generative Adversarial Networks

no code implementations25 Dec 2021 Ziyang Liu, Zhengguo Li, Xingming Wu, Zhong Liu, Weihai Chen

The proposed method, named DSRGAN, includes a well designed detail extraction algorithm to capture the most important high frequency information from images.

Generative Adversarial Network Image Super-Resolution

PTR-PPO: Proximal Policy Optimization with Prioritized Trajectory Replay

no code implementations7 Dec 2021 Xingxing Liang, Yang Ma, Yanghe Feng, Zhong Liu

In addition, by analyzing the heatmap of priority changes at various locations in the priority memory during training, we find that memory size and rollout length can have a significant impact on the distribution of trajectory priorities and, hence, on the performance of the algorithm.

Towards Comprehensive Monocular Depth Estimation: Multiple Heads Are Better Than One

no code implementations16 Nov 2021 Shuwei Shao, Ran Li, Zhongcai Pei, Zhong Liu, Weihai Chen, Wentao Zhu, Xingming Wu, Baochang Zhang

In this work, we investigate into the phenomenon and propose to integrate the strengths of multiple weak depth predictor to build a comprehensive and accurate depth predictor, which is critical for many real-world applications, e. g., 3D reconstruction.

3D Reconstruction Ensemble Learning +2

Discriminative and Semantic Feature Selection for Place Recognition towards Dynamic Environments

no code implementations18 Mar 2021 Yuxin Tian, Jinyu Miao, Xingming Wu, Haosong Yue, Zhong Liu, Weihai Chen

In this paper, we address the challenges of place recognition due to dynamics and confusable patterns by proposing a discriminative and semantic feature selection network, dubbed as DSFeat.

feature selection Visual Place Recognition

On the Mutual Interference between Spaceborne SARs: Modeling, Characterization, and Mitigation

no code implementations14 Oct 2020 Huizhang Yang, Mingliang Tao, Shengyao Chen, Feng Xi, Zhong Liu

Based on the low rank model, we show that two methods, i. e., principle component analysis and its robust variant, can be adopted to efficiently mitigate the artefact via processing in image domain.

Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach

1 code implementation24 May 2019 Changjun Fan, Li Zeng, Yuhui Ding, Muhao Chen, Yizhou Sun, Zhong Liu

By training on small-scale networks, the learned model is capable of assigning relative BC scores to nodes for any unseen networks, and thus identifying the highly-ranked nodes.

Community Detection Decoder +1

VMAV-C: A Deep Attention-based Reinforcement Learning Algorithm for Model-based Control

no code implementations24 Dec 2018 Xingxing Liang, Qi. Wang, Yanghe Feng, Zhong Liu, Jincai Huang

Recent breakthroughs in Go play and strategic games have witnessed the great potential of reinforcement learning in intelligently scheduling in uncertain environment, but some bottlenecks are also encountered when we generalize this paradigm to universal complex tasks.

Deep Attention Model-based Reinforcement Learning +4

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