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2024 China Automation Congress, CAC 2024

作者： Zhang, Zhenguo Cao, Yuanzhouhan Zhang, Hui Chen, Naiyue Ren, Chao Li, Yidong Key Laboratory of Big Data & Artificial Intelligence in Transportation Beijing Jiaotong University Ministry of Education China School of Computer Science and Technology Beijing Jiaotong University China College of Electronics and Information Engineering Sichuan University China

ISBN: (纸本)9798350368604

3D object detection is one of the fundamental perception tasks in autonomous driving. In recent years, perception in the Bird’s Eye View (BEV) has attracted tremendous attention due to its outstanding ability in preserving geometric cues. However, like other supervised learning tasks, BEV-based perception suffers from inadequate labeled training data. In this work, we propose the Vision BEV-MAE, a self-supervised learning approach for BEV perception from camera images. Specifically, we design a feature-level masking strategy to mask the mid-level representations, i.e., the encoded BEV features instead of directly masking input multi-view images, and reconstruct BEV features with a light-weight decoder. Our Vision BEV-MAE is independent of any geometric data such as point clouds, or any additional supervision, and can be easily implemented within any state-of-the-art BEV perception frameworks to improve the performance. We conduct experiments on benchmark dataset to validate our approach. © 2024 IEEE.

关键词： Self-supervised learning

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MADNet: Based on Multiple Attention and Dilated Convolution for CSI Feedback

MADNet: Based on Multiple Attention and Dilated Convolution ...

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2023 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2023

作者： Zhao, Xiang Yang, Chuansheng Wang, Chao Wang, Xiaoshuang School of Information Engineering Zhejiang Ocean University Zhoushan China Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province China

ISBN: (纸本)9781510667662

The massive multiple-input and multiple-output (MIMO) system based on channel state information (CSI) is the core technology of next-generation communication. As the complexity of the CSI matrix gradually increases, CSI feedback becomes more challenging. CSI feedback technology based on deep learning (DL) has been successful in frequency-division duplex (FDD) MIMO systems. In this paper, we propose a complex-valued lightweight neural network MADNet for CSI feedback. The network is based on an encoder-decoder structure, which enriches the information extraction of the CSI matrix by adopting channel information extraction modules and coordinates information extraction modules, which reduces the computational complexity of the network by using lightweight convolution. Experimental results show that our network outperforms most CNN-based network architectures at multiple compression rates and performs significantly better in both indoor and outdoor scenarios. © 2023 SPIE.

关键词： Channel state information

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LIGI: low-light image enhancement via gray image

LIGI: low-light image enhancement via gray image

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2023 International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2023

作者： Sun, Yue Ye, Qihong Chen, Hongming Wang, Xiaoshuang School of Information Engineering Zhejiang Ocean University Zhoushan China Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province China

ISBN: (纸本)9781510671881

Low-light images are usually affected by problems such as low illumination, noise, and color distortion, resulting in unsatisfactory image enhancement. Low-light image enhancement aims to improve the quality and visibility of low-light images. In this paper, a gray image based low-light image enhancement deep learning method is proposed, which converts the low-light image to gray image and then extracts the important information in the image as branches to assist the main network. To achieve better enhancement performance, we proposed denoising center block (DC block), which focusing on color restoration and information preservation. The experimental results show that the method proposed in this paper outperforms the existing low-light image enhancement methods in both objective and subjective evaluation metrics, and can effectively improve the clarity, contrast, and color reproduction of low-light images. © 2023 SPIE. All rights reserved.

关键词： Image enhancement

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Flatness Loss for Image Dehazing

Flatness Loss for Image Dehazing

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2023 International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2023

作者： Zhang, Chenyu Ye, Qihong Chen, Hongming Wang, Xiaoshuang School of Information Engineering Zhejiang Ocean University Zhoushan China Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province China

ISBN: (纸本)9781510671881

In recent years, significant progress has been made in image dehazing, but most dehazing convolutional neural networks only learn from hazy images to the corresponding feature maps of clean images, ignoring the details of the images. In this paper, a new flatness loss function is proposed for single-image dehazing, thereby improving the overall effect of dehazing results. This loss function allows the model to focus on the texture features of hazy images and clean images, and supervises edge information by reducing the flatness difference between clean pixels and blurred pixels. The experiments on the benchmark dataset using the flatness loss function on the single-image dehazing model show that this method can effectively improve the quantitative performance of the model. © 2023 SPIE. All rights reserved.

关键词： Pixels

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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Mal-Net: Multi-scale feature extraction and attention mechanism lightweight network for CSI feedback 3

Mal-Net: Multi-scale feature extraction and attention mechan...

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3rd International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： Song, Jinjie Chen, Hongming Zhao, Xiang Zhang, Chenyu Wu, Yiting Ye, Qihong School of Information Engineering Zhejiang Ocean University Zhoushan China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province China

ISBN: (纸本)9781510668522

Massive multiple-input multiple-output (MIMO) system is one of the wireless technologies with great research significance based on channel state information (CSI). As the complexity of the CSI matrix increases, CSI feedback faces many challenges and limitations. This paper proposes a neural network composed of the encoder-decoder framework, which can effectively compress and recover CSI. The network utilizes a channel information generation module and multi-scale feature extraction module to enhance the information acquisition ability of the CSI matrix. At the same time, it reduces the computational complexity of the network by using lightweight architecture. Experimental results show that the network outperforms other advanced deep-learning methods at multiple compression rates. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Channel state information

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Design and implementation of a Cesium-based 3D visualization platform for the Yellow River dam bank

Design and implementation of a Cesium-based 3D visualization...

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2022 International Conference on Signal Processing, computer Networks, and Communications, SPCNC 2022

作者： Zuo, Xianyu Sun, Huaiyuan Zhang, Lei Dang, Lanxue Liu, Yang Xie, Yi Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan Universfiity Kaifeng475001 China Zhong Ke Langfang Institute of Spatial Information Applications Langfang065000 China

ISBN: (纸本)9781510664616

Flood control is one of the most important operations of Yellow River control. The dam bank danger in flood season every year has the characteristics of time-varying, multi-dimension and complex situation, which poses a certain threat to people's life and property safety. For timely, flood control engineering data quickly and implement management interface of information visualization, effectively reduce the labor intensity of artificial working risk, this paper puts forward a kind of based on three-dimensional library of Cesium Huang Heba shore the realization method of 3D visualization platform adopted the aerial drones to create dam shore entity 3D model, Combined with WebGIS technology and front-end technology, the system's 3D expression is realized. Through interface design and system function development, the system has three-dimensional model display,with the functions of dam bank cruise and measurement tools, the platform has strong sense of reality and good interaction effect, which realizes the three-dimensional visual display of the Madu control project in Zhengzhou, Henan Province. © 2023 SPIE.

关键词： Flood control

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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Enhancing Drug-Drug Interaction Prediction by knowledge Graph Embedding 23

Enhancing Drug-Drug Interaction Prediction by Knowledge Grap...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Liu, Chao Wang, Xizhao Liu, Qiang Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518000 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798331528041

Drug-drug interactions (DDIs) refer to the synergistic or antagonistic effects between different drugs. Synergistic effects can enhance therapeutic efficacy, while antagonistic effects may reduce efficacy or even trigger adverse reactions, worsening the patient's condition. Most existing DDI prediction studies overlook the features of chemical bonds between atoms within drug molecules and the interaction types between drug pairs, which to some extent limits the accuracy and reliability of the predictions. In view of this, we have designed a novel framework called KGE-DDI, which leverages knowledge graph embedding (KGE) technology to enhance the prediction of DDIs. Specifically, for individual drug molecules, KGE-DDI first integrates the features of atoms and chemical bond. Then, by introducing attention mechanisms and graph neural network technology, it learns representations of the drug molecules. For pairs of drug molecules, KGE-DDI models their interactions utilizing atom-level Pearson correlation matrices. Finally, it predicts the interactions between drug pairs by employing our designed scoring function. We conducted comprehensive experiments under both transductive and inductive settings, and the results demonstrate the effectiveness and superiority of KGE-DDI in three scenarios: (existing drug, existing drug), (new drug, new drug), and (new drug, existing drug). © 2024 IEEE.

关键词： knowledge graph

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Domain Adaptation Random Vector Functional Link for White Blood Cell Classification 23

Domain Adaptation Random Vector Functional Link for White Bl...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Wang, Qin Wang, Xi-Zhao Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798331528041

There's growing recognition of how machine learning can revolutionize the precision and swiftness of clinical diagnoses by improving the classification of white blood cells. However, a machine learning model specialized in white blood cell classification demands access to a large trove of well-annotated data. This poses a challenge, as in the medical field, annotating data comprehensively comes with a high cost in terms of both money and time. To circumvent this, domain adaptation (DA) techniques are employed to leverage similar cell images from related domains, thereby boosting the model's performance in white blood cell classification in specific contexts. Among the myriad DA approaches available for this application, including TCA-based and deep learning methods, all typically suffer from lengthy training durations. Addressing this challenge, we introduce a novel DA method inspired by the random vector functional link (RVFL) network, a type of neural network characterized by its random weights. Our proposed method, named Domain Adaptation Random Vector Functional Link (DA-RVFL), capitalizes on the efficiency of RVFL networks to enhance white blood cell classification. This innovative approach shows some insights for future research in the efficient and effective classification of white blood cells. © 2024 IEEE.

关键词： Deep learning

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