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3rd International Conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024

作者： Wang, Yi Luo, Xiaohui School of Computer and Software Engineering Xihua University Sichuan ChengDu China

ISBN: (纸本)9798400707308

Deep neural networks trained on large datasets have achieved good results in image denoising. However, networks trained on specific datasets often have poor generalization, which is not conducive to practical applications. In addition, most denoisers often ignore the detail differences between image texture areas and smooth areas during the denoising process, resulting in the loss of details in texture areas. In this paper, we propose a zero-shot denoising network (HL-Net) with a Texture Noise Attention module (TNA) and a hybrid learning architecture. TNA allows the network to better extract noise information from texture areas without destroying their details, while the hybrid learning architecture uses two denoising methods for image smooth areas and texture areas: content learning LC (learning non-noise information in noisy images) and noise learning LN (learning noise information in noisy images), which can better preserve the image details in complex texture areas while extracting content information from smooth areas of noisy images. Additionally, the loss function proposed for the zero-sample hybrid learning network uses residual loss, consistency loss, and mean loss, enabling the network to better perform content learning and noise learning. Experiments show that our method outperforms most zero-sample denoising methods on various types of synthetic noise datasets and real-world noise datasets. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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EchoGCN: An Echo Graph Convolutional Network for Skeleton-Based Action Recognition 27th

EchoGCN: An Echo Graph Convolutional Network for Skeleton-Ba...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Qian, Weiwen Huang, Qian Li, Chang Chen, Zhongqi Mao, Yingchi College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9783031783531

Graph Convolutional Networks (GCNs) have attracted considerable attention in the realm of human action recognition. However, conventional GCNs-based methods typically struggle to construct adjacency matrices that capture diverse semantics, thus leading to performance limitations. To tackle this issue, we propose the Echo Graph as a set of adjacency matrices, which includes both hierarchical and global graphs. Specifically, our hierarchical graph exploits the hierarchical information based on the selected central broadcast nodes, aggregating joints dispersed across considerable physical distances into a unified semantic space. The global graph we construct transcends the limitations imposed by physically defined topological structures, delving into comprehensive information exchange among nodes. Additionally, we propose node activation and hierarchical activation model. These activations aim to prominently highlight crucial nodes and edges for specific samples. Finally, we incorporate Margin ReLU distillation to improve computational efficiency and design a four-stream integration using only the joint and bone data streams from two central broadcast nodes. Based on the aforementioned components, we propose EchoGCN, capable of extracting representative skeleton features. Experimental results on three datasets (NTU-RGB+D 60, NTU-RGB+D 120, and NW-UCLA) demonstrate that our model achieves state-of-the-art performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Musculoskeletal system

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Improved method based on complex background YOLOv8 optimization 4

Improved method based on complex background YOLOv8 optimizat...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Ma, Tingsong Ye, Xinyu Xiao, Bin School of Computer and Software Engineering Xihua University Chengdu China

ISBN: (纸本)9798331541729

Establishing a lightweight yet high-precision object detection algorithm is essential for accurately assessing the helmet-wearing status of workers in complex industrial environments. Helmet detection poses significant challenges due to several factors, including small target size, intricate backgrounds, and the need to balance model compactness with detection accuracy. In this article, we propose an improved method based on the YOLOv8 model. Our approach enhances the model's capability to detect small targets within complex settings. Additionally, we introduce a novel module that integrates Depthwise Convolution (DWConv) with the RepVGG network structure into the backbone feature extraction network. This integration aims to reduce model complexity while improving accuracy. To further enhance helmet detection performance, we incorporated a C2fDFC attention mechanism module into the feature extraction network. This addition reduces both parameter count and computational load, ultimately leading to superior helmet detection *** used the Hard Hat Workers Object Detection Dataset released by Roboflow, which is a dataset of hard hats in complex situations, to verify our research method. Our proposed model achieved an optimal mean Average Precision at IoU 0.50 (mAP50) of 66.1%. These results demonstrate the effectiveness of our method in achieving lightweight deployment alongside high-precision helmet detection. © 2024 IEEE.

关键词： Attention mechanism Helmet inspection RepVit YOLOV8

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SSTHyper: Sparse Spectral Transformer for Hyperspectral Image Reconstruction 17th

SSTHyper: Sparse Spectral Transformer for Hyperspectral Ima...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Xu, Meng Lin, Mingying Ren, Qi Jia, Sen College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9789819609109

Transformer-based methods have improved the quality of hyperspectral images (HSIs) reconstructed from RGB by effectively capturing their remote relationships. The self-attention mechanisms in existing Transformer models have not fully considered the spatial sparsity and spectral continuity characteristics of HSIs and fail to effectively filter out significant features, resulting in lower-quality reconstruction results. This paper proposes a sparse spectral transformer model for HSI reconstruction (SSTHyper) to address this limitation, adaptively preserving crucial features. The network consists of an encoder-decoder structure capable of learning shallow and deep spatial-spectral priors, primarily composed of sparse spectral self-attention groups. Introducing a sparse spectral self-attention mechanism allows adaptive masking of non-significant details, enhancing reconstruction accuracy. Meanwhile, a lightweight cross-level fusion network is proposed to reduce model parameters and computational costs to enhance spatial-spectral feature extraction. Experimental results on two benchmark datasets demonstrate the outstanding performance of the proposed method. The code will be released at SSTHyper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image reconstruction

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A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

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computers, Materials & Continua 2024年第5期79卷 2945-2970页

作者： Muhammad Salman Qamar Ihsan ulHaq Amil Daraz Atif MAlamri Salman A.AlQahtani Muhammad Fahad Munir Department of Electrical and Computer Engineering International Islamic UniversityIslamabad44000Pakistan School of Information Science and Engineering NingboTech UniversityNingbo315100China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable *** data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network *** mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring *** unique determination of this study is the shortest path to reach *** the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static *** this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the *** methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide *** addition,a method of using MS scheduling for efficient data collection is *** simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

关键词： Wireless Sensor Networks(WSNs) mobile sink(MS) rendezvous point(RP) machine learning Artificial Neural Networks(ANNs)

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Spectral Modality-Aware Interactive Fusion Network for HSI Super-Resolution 17th

Spectral Modality-Aware Interactive Fusion Network for HSI ...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Xu, Meng Mao, Jiayou Mo, Ziqian Fu, Xiyou Jia, Sen College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9789819609109

Due to the limitations of current spectral imaging equipment in acquiring high-resolution hyperspectral images (HR-HSIs), a common approach is to fuse low-resolution hyperspectral images (LR-HSIs) with high-resolution multispectral images (HR-MSIs). However, most existing methods have not fully taken into account the correlation and discrepancy in modality between hyperspectral images (HSIs) and multispectral images (MSIs). To address this limitation, we propose an innovative spectral modality-aware interactive fusion network (SMIF-NET) for comprehensive extraction of spectral information and seamless feature fusion. First, we introduce the spectral modality-aware transformer (SMAT) with a dual-attention mechanism to compute spectral self-similarity and cross-spectral correlation. Second, we apply the interactive spatial-spectral feature fusion (IS2F2) to fuse the acquired high-level spectral and spatial features. This fusion technique combines spatial-wise and channel-wise squeeze and excitation to achieve seamless integration of spatial-spectral information. Finally, the extensive experiments on three datasets demonstrate the superior performance of SMIF-NET in both visual and quantitative assessments compared to eight state-of-the-art (SOTA) fusion-based methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image fusion

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A Sentiment Classification Method Focused on Emotional Regions and Image-text Interaction Fusion 4

A Sentiment Classification Method Focused on Emotional Regio...

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4th International Conference on Electronic Information engineering and computer Technology, EIECT 2024

作者： Yuan, Xianzhi Liu, Kejian School of Computer and Software Engineering Xihua University Chengdu China

ISBN: (纸本)9798331528850

Image-text information has become mainstream on current social platforms, with image-text sentiment analysis leveraging the complementarity between different modalities to improve sentiment classification performance. In current tasks for image-text sentiment analysis, images often contain significant emotion-irrelevant noise, so focusing on emotional regions within images can greatly impact sentiment classification. Facial expressions, in particular, play a critical role in determining sentiment polarity. This paper proposes an image-text sentiment analysis model that emphasizes facial regions and image-text interactive fusion. In this model, BERT and ResNet are used to extract text and image features, while a convolutional model fine-tuned for facial emotion recognition captures facial expression features. By incorporating a global attention mechanism and a multi-modal fusion module, the model selectively filters and integrates emotional signals from cross-modal information, enabling effective classification of image-text sentiment tendencies. Experimental results on the MVSA datasets show an average improvement in accuracy and F1-score by 1.18% and 1.59%, respectively, compared to the best-performing baseline model. © 2024 IEEE.

关键词： Image classification

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CLIP-Based Cross-Level Semantic Interaction and Recombination Network for Composed Image Retrieval 27

CLIP-Based Cross-Level Semantic Interaction and Recombinatio...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Liu, Zhihang Huang, Qiang Zhu, Yingying College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781643685489

Composed image retrieval seeks to retrieve a target image that fulfills both modalities based on the user's query, which comprises a modified text and a reference image. Although existing studies introduce novel multi-modal feature fusion techniques at the global or local level, they ignore exploring the cross-level semantic correspondence and recombining the original and target features in the reference image and the modified text, and the problems of modal inconsistency. To address these issues, we propose a CLIP-based Cross-Level Semantic Interaction and Recombination Network (SeIR). Specifically, we first resort the image and text encoders of the CLIP pre-trained model to narrow the modal gap between image and text, and extract both global and local features. We also introduce a cross-modal attention mechanism to screen out original and target features by exploring the semantic correlation between the cross-level image and text features. Subsequently, to alleviate the modal difference between the generated composed query representation and the target image, we utilize an affine transformation technique to recombine the original and target features from the image and text. Extensive experiments on the FashionIQ and CIRR benchmark datasets demonstrate the competitive performance of the proposed SeIR compared to the state-of-the-art methods. © 2024 The Authors.

关键词： Affine transforms

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Depression Detection Based On Text And User Networksa 7

Depression Detection Based On Text And User Networksa

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7th World Conference on Computing and Communication Technologies, WCCCT 2024

作者： Chen, Jingyu Peimin, Lv Chen, Qi Yang, Song Zhang, Xinyun Wu, Yue Xihua University School of Computer and Software Engineering Chengdu China

ISBN: (纸本)9798350372328

The extensive utilization of social networks presented an opportunity for detecting depression in a timely and cost-effective manner. In that paper, we proposed TUNDD, a depression detection method based on text and user network, which leveraged social network data. TUNDD comprised three modules: the text embedding module utilized BERT to encode post text, the graph perception module applied GAT to explore potential relationships among users, and the classification module combined post text and user vectors, transforming them into higher-dimensional vectors through BiRNN and generating results through softmax. Experimental results demonstrated that TUNDD outperformed five baseline experiments on two datasets, achieving accuracy rates of 86.3% and 84.7%, respectively. Furthermore, ablation experiments revealed that BERT, GAT, and the number of neural network layers in GAT significantly impacted the effectiveness of depression detection. © 2024 IEEE.

关键词： Embeddings

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A Motion Capture Quality Comparison Between Rokoko and Kinect 6

A Motion Capture Quality Comparison Between Rokoko and Kinec...

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6th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Li, Monica M.Q. Polytehcnique Montreal Department of Computer and Software Engineering Montreal Canada

ISBN: (纸本)9798350389692

In human machine interaction tasks, the quality of motion capture plays a critical role. Rokoko Motion Capture System (Rokoko) is a relatively economic motion capture device and has been utilized in various areas of motion-related research. In this study, we test the representative ability of the products captured by Rokoko and Microsoft Kinect v2 (Kinect). Three non-professional actors wore the Rokoko to do three kinds of activities: walking with normal velocity, jumping with vertical acceleration and rotation, by displaying different body functions. Motion data were recorded using these two devices simultaneously. We compared the pros and cons of the results of the captured motions by these two devices and provide suggestions and precautions using Rokoko Smartsuit Pro for motion capture activities for researchers. © 2024 IEEE.

关键词： Human robot interaction

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