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13th International Conference on intelligent Control and Information processing, ICICIP 2025

作者： Huang, Zhixuan Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

Artificial intelligence (AI) has been a key research area since the 1950s, initially focused on using logic and reasoning to create systems that understand language, control robots, and offer expert advice. With the rise of big data and deep learning, AI has advanced in applications like recommendation systems, image recognition, and machine translation, primarily through optimizing loss functions in deep neural networks to improve accuracy and reduce training *** descent is the core optimization method but faces challenges like slow convergence and local minima. To overcome these, algorithms like Momentum, AdaGrad, RMSProp, Adadelta, Adam, and Nadam have been developed, introducing momentum and adaptive learning rates to accelerate convergence. This paper presents a new optimization algorithm that combines the strengths of Adam and AdaGrad, offering better adaptability to different learning rates. © 2025 IEEE.

关键词： Contrastive Learning

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Attitude Detection Model Based on Improved High Resolution Network 13

Attitude Detection Model Based on Improved High Resolution N...

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13th International Conference on intelligent Control and Information processing, ICICIP 2025

作者： Shi, Dongliang Li, Yuhan Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

2D human posture detection, a challenging task in computer vision, aims to identify human body key points and is widely used. The HRNet model performs well in human key point detection. To enhance its effect in human posture detection and address suboptimal prediction accuracy in interactive occlusion, this paper combines HRNet and Transformer. The Transformer's attention mechanism handles complex image dependencies, and HRNet obtains image features. By embedding the Transformer's attention mechanism module into HRNet's backbone network and integrating a self-designed double-branch structure and convolutional attention module, an improved HRNet model is constructed. The DConvAttention module processes extracted features, and the convolutional attention mechanism balances image frequencies, enhancing processing and discrimination of complex backgrounds and occlusions and improving detection performance. In tests of complex and occluded images, the improved HRNet Local model has a 4.2M parameter increase, 0.5G FLOPs increase, and 1% mAP increase compared to HRNet, with the same recall rate. The improved HRNet Global model has 11M more parameters, 1.1G more FLOPs, a similar or slightly higher recall rate, and a 2% higher mAP value. © 2025 IEEE.

关键词： Face recognition

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Improved LYT-Net image Enhancement Model for Underwater Robotics Vision 13

Improved LYT-Net Image Enhancement Model for Underwater Robo...

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13th International Conference on intelligent Control and Information processing, ICICIP 2025

作者： Li, Wenjie Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Güttin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

—Although underwater robots can replace humans to explore the ocean which is rich in resources but fraught with unknown risks, there are phenomena such as monotonous colors, complex backgrounds and uneven illumination in the underwater environment, which makes it difficult for underwater robots relying on the optical vision system to obtain clear images and is not conducive to subsequent recognition and grasping tasks. In this paper, relevant preparatory work was carried out for underwater image processing applicable to underwater robots, and the underwater imaging model as well as other underwater image processing models were analyzed. Subsequently, a method combining the underwater physical model and the dark channel image was proposed to improve LYT-Net, enabling it to be applicable to mobile embedded devices such as underwater robots. After training and testing on the real datasets LSUI and UIEB, our model achieved good results on important indicators such as PSNR and SSIM. Meanwhile, it met the theoretical requirements of mobile devices in terms of FLOPs, Params and single-frame processing time, which verified its robustness and real-time processing ability in various underwater environments. © 2025 IEEE.

关键词： Underwater imaging

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Underwater image Restoration Based on Improved SwinIR Model 13

Underwater Image Restoration Based on Improved SwinIR Model

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13th International Conference on intelligent Control and Information processing, ICICIP 2025

作者： Lu, Yanke Li, Fang Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

image restoration is a classic foundational visual task, aimed at recovering damaged images, such as those affected by compression, blurring, or noise, to high-definition clarity. Although current image enhancement techniques largely rely on convolutional neural networks, the efficient capabilities of Transformers demonstrated in complex visual tasks have not yet been extensively applied in the field of image restoration. This paper presents an optimized image restoration strategy based on the SwinIR model, which is applied to the task of underwater image restoration. In the shallow feature extraction phase, deep separable convolution technology is employed;while in the deep feature extraction phase, a Spatial Gated Feedforward Network (SGFN) is utilized. The experimental data suggests that the novel approach surpasses conventional techniques in effectiveness and has effectively minimized both the model's parameter quantity and computational demands. © 2025 IEEE.

关键词： Convolution

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Linear self-attention with multi-relational graph for knowledge graph completion

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Applied Intelligence 2025年第8期55卷 1-15页

作者： Liu, Weida Qiang, Baohua Chen, Ruidong Xie, Yuan Chen, Lirui Chen, Zhiqin Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China

Knowledge graph completion (KGC) aims to infer missing facts based on the existing knowledge. Graph Convolutional Networks (GCNs) have gained significant traction due to their proficiency in effectively modeling graph structures, especially within the realm of Knowledge Graph Completion (KGC). In GCN-based KGC methodologies, GCNs are initially employed to generate comprehensive representations of entities, followed by the application of Knowledge Graph Embedding (KGE) models to elucidate the interactions among entities and relations. However, most GCN-based KGC models ignore the long-range pairwise relationships in the graph. To address these limitations and enhance KGC, we propose a model called Linear Self-Attention with Multi-Relational Graph Network (LTRGN). Specifically, this model merges GCN and linear self-attention to serve as the encoder. This model introduces a linear self-attention that can capture long-range node dependencies without introducing excessive computational overhead. Furthermore, we implement an attention mechanism designed to better assess the significance of various neighboring nodes relative to the source node. We demonstrate the effectiveness of the proposed LTRGN on the standard FB15k-237, WN18RR, Kinship, and UMLS datasets. On the dense graphs Kinship and UMLS, the MRR of our model improves by 1.3% and 4.1%, respectively, while Hits@1 increases by 1.7% and 6.4% compared to the best-performing model. The results show the efficacy of the model for the KGC task. The code is released at https://***/lixianqingliuyan/LTRGN. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Knowledge graph

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Research on Collaborative Filtering Algorithm of E-commerce Based on Flink 13

Research on Collaborative Filtering Algorithm of E-commerce ...

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13th International Conference on intelligent Control and Information processing, ICICIP 2025

作者： Zheng, Haohao Luo, Xiaonan Zhu, Xiaoshu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

In online shopping, a person's interest in a product is closely related to whether they will purchase it Analyzing people's interest in various products on time, along with product recommendations, can increase purchase rates. However, in many cases, We only know user implicit feedback, such as browsing or favoriting items, and cannot directly access user explicit feedback, such as product ratings. Moreover, users have limited exposure to products, resulting in a sparse user- item matrix. The traditional Alternating Least Squares (ALS) algorithm can partially address the issue of matrix sparsity, but its accuracy in recommending products based on implicit feedback alone is insufficient This paper proposes an improved ALS algorithm that converts implicit feedback into explicit ratings. We calculate the browsing frequency of a product based on user browsing counts. For a given product, we represent user preference by summing the browsing frequencies of all products with browsing frequencies less than or equal to that of the product. Then, by multiplying the preference level by the product's maximum rating, we derive a product rating, achieving a transformation from implicit feedback to explicit scores. Additionally, traditional collaborative filtering algorithms often use offline data, which may not align with user current preferences. To improve the algorithm's accuracy, we also utilize Flink for real-time data processing, allowing us to capture the most recent feedback data. This study tests the approach using the UserBehavior dataset from the publicly available Tianchi dataset. Experimental results indicate improvements in both Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). © 2025 IEEE.

关键词： Collaborative filtering

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Low-light few-shot object detection via curve contrast enhancement and flow-encoder-based variational autoencoder

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Neural Computing and Applications 2025年第10期37卷 7261-7278页

作者： Jiang, Zetao Jin, Xin Kang, Junjie The Key Laboratory of image and graphic intelligent processing Guilin University of Electronic Technology Guangxi Guilin541004 China Wuhan Service Department Sinopec Shared Services.LTD.Nanjing Branch Jiangsu Nanjing210018 China Co. LTD Geely Group Zhejiang Ningbo315336 China

Aiming at the problem of insufficient samples in low-light object detection in some environments, a low-light few-shot object detection method based on curve contrast enhancement and flow-encoder-based variational autoencoder (CCEFVAE) is proposed. Our approach involves designing a CCE module to enhance the detailed features and contrast of low-light images by deriving a relationship expression between the enhanced image and the low-light image through the recursive relationship of high-order curves. The lumination estimation module in the CCE module estimates the parameters of the expression to calculate the pixel values of the enhanced image. Moreover, we propose an FVAE module to improve the decoupling of support features by combining the flow model encoder with the variational autoencoder, facilitating subsequent feature aggregation and classification. To ensure the consistency of the loss function of the flow model with the few-shot object detection loss, we design a negative Jacobian determinant transformation function. This enables direct addition of the two losses, allowing for unified optimization. Experimental results demonstrate that our proposed algorithm outperforms mainstream few-shot object detection models by an average of 13.1–23% in average after training on the low-light dataset (ExDark), and shows an average improvement of 5.8% compared to the state-of-the-art (SOTA) few-shot object detection model VFA. When trained on the normal lighting dataset (PASCAL VOC), the proposed algorithm exhibits a 1.7% improvement in average compared to VFA. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Curve contrast enhancement Few-shot object detection Flow-encoder-based variational autoencoder Low-light environment

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Optimization algorithm for global k-means algorithm 13

Optimization algorithm for global k-means algorithm

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13th International Conference on intelligent Control and Information processing, ICICIP 2025

作者： Li, Sen Li, Fang Luo, Xiaonan School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

The k-means method is widely utilized for clustering. Its simplicity, efficacy, and swiftness make it a favored choice among clustering algorithms. It faces the challenge of sensitivity to the initial class center. The enhancement strategy for the k-means algorithm focuses on refining the initial selection of cluster centroids, no longer using its random selection of initial class center points, but starting from the entire dataset, choosing the most distant point to serve as the starting cluster centroid. This can also prevent the local optimization problem of the k-means algorithm. The improved algorithm proposed in this article is an effective method that can select the initial class center point from a global perspective and reduce computation time. The suggested approach has undergone rigorous testing and comparative analysis across multiple datasets, yielding commendable outcomes. © 2025 IEEE.

关键词： Optimization algorithms

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Tomato Leaf Disease Identification and Growth Dataset Augmentation Based on UAE-GAN 13

Tomato Leaf Disease Identification and Growth Dataset Augmen...

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13th International Conference on intelligent Control and Information processing, ICICIP 2025

作者： Li, Xin Li, Fang Luo, Xiaonan School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

To delve into the characterization of growth disorders in different crops, it is important to support the model with a large amount of image data that includes a variety of disease types and disease levels to capture the typical and subtle differences of various diseases on plant leaves. However, the actual process of gathering data is challenging, sample coverage is challenging to accomplish, data capture is impeded, and the quality of the data is subpar. This work aims to address the issue of data shortages by employing technical methods. In particular, we creatively investigated the UAE-GAN approach, which naturally combines CycleGAN, U-Net, Variational Autoencoder VAE, and Autoencoder to increase the data. Among these, U-Net can precisely extract the small details of disease locations in crop photos and provide a strong basis for further processing thanks to its special codec architectural benefits. The Variational Autoencoder (VAE) significantly enhances the diversity of data by mapping the image to the latent space and sampling based on a certain probability distribution, so producing new image samples that are distinct from the original image yet inherently connected. Learning the coding and decoding of the original image is the foundation of autoencoders. If a mild disruption is introduced into the coding process, it can achieve data augmentation in another dimension and create a sequence of new images with just little modifications to the original image. The aforementioned models are closely linked with CycleGAN to efficiently map and convert in a variety of picture domains and to fully leverage CycleGAN's remarkable unsupervised image conversion capabilities. The perception ability, feature capture ability, and information conversion ability of the fusion model for crop image data are significantly improved, and the key elements of each link in the data enhancement process are comprehensively considered to ensure that the generated new image data can not o

关键词： Fruits

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Integrating the dynamic optimization strategies of Adam and AdaGrad

Integrating the dynamic optimization strategies of Adam and ...

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International Conference on intelligent Control and Information processing (ICICIP)

作者： Zhixuan Huang Xiaonan Luo Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

关键词： Training Heuristic algorithms Optimization methods Machine translation Logic Artificial intelligence Robots Recommender systems Convergence intelligent control

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