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Spread-based elite opposite swarm optimizer for large scale optimization

Cognitive Robotics 2022年 2卷 112-118页

作者： Zhang, Li Tan, Yu Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin University of Electronic Technology Guilin541004 China

To prevent the traditional particle swarm optimizer (PSO) from inefficient search in complex problem spaces, this paper presents a novel spread-based elite opposite swarm optimizer (SEOSO) for large scale optimization. Inspired by the dandelion seeds in nature, the seeds can randomly spread by wind and grow better for the next generation. To achieve this, the spread learning and elite opposite learning are introduced in SEOSO. In spread learning, the particles are divided into some subswarms and these subswarms can exchange the particles to get more useful information that improves the diversity of the swarm. In elite opposite learning, the opposite position of the particle is used to exclude the worse direction. The experiments are conducted on 35 benchmark functions to evaluate the performance of SEOSO in comparison with several state-of-the-art algorithms. The comparative results show the effectiveness of SEOSO in solving large scale optimization problems. © 2022

关键词： Particle swarm optimization (PSO)

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An Improved Seq-Deepfake Detection Method 1st

An Improved Seq-Deepfake Detection Method

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1st International Conference on Applied Intelligence, ICAI 2023

作者： Deng, Zhenrong You, Kang Yang, Rui Hu, Xinru Chen, Yuren Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin University of Electronic Technology Guilin541004 China Guangxi Construction Industry Mostly Guangxi China

ISBN: (纸本)9789819709021

Facial processing technology's ability to generate realistic human faces poses significant societal risks when exploited maliciously. Deep face fraud detection relies on deep learning to meticulously scrutinize the manipulation sequence of fake faces, uncovering deceptive traces. This study focuses on Detecting Sequential DeepFake Operations (Seq-DeepFake), transforming the face detection task into an image-to-sequence *** enhance detection accuracy, this paper introduces a Seq-DeepFake detection method. The Seq-DeepFake Transformer model's activation function is refined, incorporating the Rectified Randomized Leaky Unit (RReLU) to address learning rate challenges associated with negative input values. Furthermore, diverse attention mechanism modules are integrated into the backbone network, forming the innovative CLSP-Resnet-50 model. Experimental results demonstrate the efficacy of the enhanced Seq-DeepFake model, employing two evaluation metrics on a deepfake dataset, showcasing improved accuracy. Comparative analysis against other real and fake face detection methods substantiates the effectiveness of the Seq-DeepFake model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fake detection

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Improved YOLOv8n Model for Detecting Helmets and License Plates on Electric Bicycles

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computers, Materials & Continua 2024年第7期80卷 449-466页

作者： Qunyue Mu Qiancheng Yu Chengchen Zhou Lei Liu Xulong Yu The College of Computer Science and Engineering North Minzu UniversityYinchuan750021China The Key Laboratory of Images and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu UniversityYinchuan750021China

Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic *** effectively monitor compliance,the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric ***,manual enforcement by traffic police is time-consuming and *** methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning *** paper proposes an enhanced model for detecting helmets and license plates on electric bicycles,addressing these *** proposedmodel improves uponYOLOv8n by deepening the network structure,incorporating weighted connections,and introducing lightweight convolutional *** modifications aim to enhance the precision of small target recognition while reducing the model’s parameters,making it suitable for deployment on low-performance devices in real traffic *** results demonstrate that the model achieves an mAP@0.5 of 91.8%,showing an 11.5%improvement over the baselinemodel,with a 16.2%reduction in ***,themodel achieves a frames per second(FPS)rate of 58,meeting the accuracy and speed requirements for detection in actual traffic scenarios.

关键词： YOLOv8 object detection electric bicycle helmet detection electric bicycle license plate detection

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Hierarchical Optimization Method for Federated Learning with Feature Alignment and Decision Fusion

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computers, Materials & Continua 2024年第10期81卷 1391-1407页

作者： Ke Li Xiaofeng Wang Hu Wang College of Computer Science and Engineering North Minzu UniversityYinchuan750021China The Key Laboratory of Images&Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu UniversityYinchuan750021China

In the realm of data privacy protection,federated learning aims to collaboratively train a global ***,heterogeneous data between clients presents challenges,often resulting in slow convergence and inadequate accuracy of the global *** shared feature representations alongside customized classifiers for individual clients emerges as a promising personalized ***,previous research has frequently neglected the integration of global knowledge into local representation learning and the synergy between global and local classifiers,thereby limiting model *** tackle these issues,this study proposes a hierarchical optimization method for federated learning with feature alignment and the fusion of classification decisions(FedFCD).FedFCD regularizes the relationship between global and local feature representations to achieve alignment and incorporates decision information from the global classifier,facilitating the late fusion of decision outputs from both global and local ***,FedFCD employs a hierarchical optimization strategy to flexibly optimize model *** experiments on the Fashion-MNIST,CIFAR-10 and CIFAR-100 datasets,we demonstrate the effectiveness and superiority of *** instance,on the CIFAR-100 dataset,FedFCD exhibited a significant improvement in average test accuracy by 6.83%compared to four outstanding personalized federated learning ***,extended experiments confirm the robustness of FedFCD across various hyperparameter values.

关键词： Federated learning data heterogeneity feature alignment decision fusion hierarchical optimization

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PFMRC: A Unified MRC Framework for Chinese Named Entity Recognition via Semantic Feature Fusion 4

PFMRC: A Unified MRC Framework for Chinese Named Entity Reco...

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4th International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Deng, Zhenrong Zhang, Jinglin Xiao, Yannan Wei, Shiwei Guilin University of Electronic Technology Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin China Nanning Research Institute Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin University of Electronic Technology Guilin China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin China

ISBN: (纸本)9798331531225

The landscape of natural language processing (NLP) has been revolutionized by advancements that enable the upstream training of knowledge-rich models for downstream tasks, particularly named entity recognition (NER). Traditional NER approaches, treating it as a sequence labeling problem, often fail to deeply understand the whole text and to capture syntactic nuances of text. To address these issues, we introduce PFMRC, a novel machine reading comprehension (MRC) framework that leverages the strengths of both contextual semantics and syntactic information for enhanced NER performance. PFMRC employs a dual-pronged feature extraction strategy, utilizing RoBERTa and BiGRU to encode text features for contextual semantic information and representing them as node features of the graph structure, and a weighted embedding representation to translate syntactic dependencies into graph-structured edge features. Our model further incorporates Graph Attention Networks (GAT) to harmonize node and edge features, facilitating a nuanced understanding of text. The integration of prior knowledge and the modeling of intricate syntactic relationships through Graph Attention Networks (GAT) not only improves entity recognition accuracy but also enriches the feature representation, leading to more robust NER outcomes. Extensive experiments on three Chinese datasets demonstrate PFMRC's superior performance over state-of-the-art models. © 2024 IEEE.

关键词： Semantics

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DKD2L: Dual Knowledge Distillation Dynamic Learning for sketch-based 3D shape retrieval

DKD2L: Dual Knowledge Distillation Dynamic Learning for sket...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Su, Yawen Bai, Jing Lin, Gan School of Computer Science and Engineering North Minzu University Yinchuan China The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission China

ISBN: (纸本)9798350368741

Sketch-based 3D shape retrieval has become a prominent area of research in computer vision, confronting challenges related to the inherent diversity and abstraction of sketches, as well as inter-domain discrepancies. This paper introduces a novel approach called Dual Knowledge Distillation Dynamic Learning (DKD2L) aimed at enhancing the extraction of spatio-temporal features for sketch-based 3D shape retrieval. We develop a temporal feature extraction network to effectively capture the dynamic temporal characteristics of sketches and improve retrieval efficiency through temporal knowledge distillation. Additionally, to tackle intra-class variation and inter-class imbalance, we apply semantic knowledge distillation, enabling the 3D shape network to guide the sketch network in capturing common semantic information. This approach facilitates precise cross-modal alignment and enhances retrieval accuracy. Extensive experiments on two benchmark datasets demonstrate that DKD2L surpasses existing state-of-the-art methods. © 2025 IEEE.

关键词： 3D Shape Retrieval Knowledge Distillation Sketch-based 3D Shape Retrieval Temporal Feature

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CDF-Net: A zero-shot 3D classification network with CLIP Decoder for Feature Fusion 24

CDF-Net: A zero-shot 3D classification network with CLIP Dec...

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2024 International Conference on intelligent Perception and Pattern Recognition, IPPR 2024

作者： Yan, Hao Bai, Jing Liu, Lu School of Computer Science and Engineering North Minzu University China The Key Laboratory of images Graphics Intelligent Processing of State Ethnic Affairs Commission China

ISBN: (纸本)9798400707315

Zero-shot 3D model classification requires neural networks to correctly classify models with unseen categories, which has important practical implications for fields such as autonomous driving and robotics. PointCLIP demonstrates that transferring knowledge from the 2D image domain to the 3D model domain is beneficial for zero-shot 3D model classification, even without incorporating new 3D-specific knowledge. However, its effectiveness is limited by the lack of 3D knowledge. As a result, subsequent improvements have focused on refining 3D-to-2D projection algorithms rather than exploiting 3D information. Our approach aims to exploit both image domain knowledge and the full potential of 3D models. To this end, we propose CDF-Net, a learnable model architecture. CDF-Net consists of two components: an initial feature encoding module using the Contrastive Language-Image Pre-training network to extract 2D knowledge from multi-view representations of 3D models, and a learnable module to extract 3D knowledge and integrate it with 2D knowledge. Evaluations on two benchmarks consisting of four datasets show that CDF-Net achieves competitive classification accuracies, demonstrating the effectiveness of our method. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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Fine-grained point cloud classification based on adaptive KNN algorithm 24

Fine-grained point cloud classification based on adaptive KN...

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2024 International Conference on intelligent Perception and Pattern Recognition, IPPR 2024

作者： Liu, Lu Bai, Jing Yan, Hao School of Computer Science and Engineering North Minzu University China The Key Laboratory of images Graphics Intelligent Processing of State Ethnic Affairs Commission China

ISBN: (纸本)9798400707315

In point cloud deep learning, local feature aggregation is crucial to improve the performance of the model. The traditional KNN (K-Nearest Neighbors) method performs local feature aggregation by fixing the K value, but often performs poorly in the face of different local structural variations. In order to overcome these limitations, this study proposes an improved KNN method that adapts to the diversity of local regions of the point cloud by adaptively adjusting the neighborhood size in order to maintain feature diversity and accuracy in regions of varying importance. Specifically, in this paper, we dynamically adjust different neighborhood ranges according to the attention weights of individual points, thus ensuring that local features are effectively captured in both important as well as non-important regions. In addition, we conduct experiments on the FG3D and ModelNet40, point cloud datasets. The overall accuracy of AK-Net reaches 96.31%, 79.85%, and 82.38% in the experimental results on the three sub-datasets Airplane, Car, and Chair of FG3D, and 94.04% on ModelNet40. Therefore, the classification performance of AK-Net is superior. © 2024 Copyright held by the owner/author(s).

关键词： Adaptive algorithms

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Parameter-free Zero-shot 3D Model Classification Based on Multi-view Representation 24

Parameter-free Zero-shot 3D Model Classification Based on Mu...

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2024 International Conference on intelligent Perception and Pattern Recognition, IPPR 2024

作者： Xi, Shuting Bai, Jing Yan, Hao School of Computer Science and Engineering North Minzu University China The Key Laboratory of Images Graphics Intelligent Processing of State Ethnic Affairs Commission China

ISBN: (纸本)9798400707315

The large-scale pre-trained models have demonstrated excellent performance in processing visual and language tasks in open-world scenarios. However, recent studies have shown that there are limitations in using comparative language-vision pretrained models for zero-shot 3D model classification, mainly in ignoring the multi-view independent information of 3D models, and using only a single template for text prompts, which fails to exploit the rich contextual information provided by the language, and restricts the matching of visual and semantic information. In order to solve these problems, this paper proposes a new framework of Parameter-free zero-shot 3D model classification based on "interaction first, fusion later", which utilizes a pre-trained visual encoder to extract multi-view visual features, and then explores contextual information from multiple perspectives with the help of semantic comprehension of a large language model, establishes fine-grained associations between independent views and prompts through view-by-view interaction with text features, and integrates the independent decision results among views through decision-level fusion strategy to realize zero-shot classification. Without any 3D training, the proposed method achieves 78.8% accuracy on the ZS3D dataset, and 53.2%, 40.3%, and 26.3% accuracy on the three sub-datasets of Ali, which verifies the generalizability and effectiveness of the proposed algorithm. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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A topic relevance-aware click model for web search

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Journal of intelligent and Fuzzy Systems 2024年第4期46卷 8961-8974页

作者： Jianping, Liu Yingfei, Wang Jian, Wang Meng, Wang Xintao, Chu College of Computer Science and Engineering North Minzu University Yinchuan China The Key Laboratory of Images Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University Yinchuan China Agricultural Information Institute Chinese Academy of Agricultural Sciences Beijing China

To better understand users’ behavior patterns in web search, numerous click models are proposed to extract the implicit interaction feedback. Most existing click models are heavily based on the implicit information to model user behaviors, ignoring the impact of explicit information between queries and documents in search sessions. In this paper, we fully consider the topic relevance between queries and documents in search sessions and propose a novel topic relevance-aware click model (TRA-CM) for web search. TRA-CM consists of a relevance estimator and an examination predictor. The relevance estimator consists of a topic relevance predictor and a click context encoder. In the topic relevance predictor, we utilize the pre-trained BERT model to model the content information of queries and documents in search sessions. Meanwhile, we use transformer to encode users’ click behaviors in the click context encoder. We further apply a two-stage fusion strategy to obtain the final relevance scores. The examination predictor estimates the examination probability of each document. We further utilize learnable filters to attenuate log noise and obtain purer input features in both relevance estimator and examination predictor, and investigate different combination functions to integrate relevance scores and examination probabilities into click prediction. Extensive experiment results on two real-world session datasets prove that TRA-CM outperforms existing click models in both click prediction and relevance estimation tasks. © 2024 – IOS Press. All rights reserved.

关键词： Forecasting

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