Gotowe bibliografie tematyczne / Convolution dilatée

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Gotowa bibliografia na temat „Convolution dilatée”

Autor: Grafiati

Data publikacji: 24 sierpnia 2024

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Convolution dilatée"

Wang, Wei, Yiyang Hu, Ting Zou, Hongmei Liu, Jin Wang, and Xin Wang. "A New Image Classification Approach via Improved MobileNet Models with Local Receptive Field Expansion in Shallow Layers." Computational Intelligence and Neuroscience 2020 (August 1, 2020): 1–10. http://dx.doi.org/10.1155/2020/8817849.

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Because deep neural networks (DNNs) are both memory-intensive and computation-intensive, they are difficult to apply to embedded systems with limited hardware resources. Therefore, DNN models need to be compressed and accelerated. By applying depthwise separable convolutions, MobileNet can decrease the number of parameters and computational complexity with less loss of classification precision. Based on MobileNet, 3 improved MobileNet models with local receptive field expansion in shallow layers, also called Dilated-MobileNet (Dilated Convolution MobileNet) models, are proposed, in which dilat

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Peng, Wenli, Shenglai Zhen, Xin Chen, Qianjing Xiong, and Benli Yu. "Study on convolutional recurrent neural networks for speech enhancement in fiber-optic microphones." Journal of Physics: Conference Series 2246, no. 1 (2022): 012084. http://dx.doi.org/10.1088/1742-6596/2246/1/012084.

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Abstract In this paper, several improved convolutional recurrent networks (CRN) are proposed, which can enhance the speech with non-additive distortion captured by fiber-optic microphones. Our preliminary study shows that the original CRN structure based on amplitude spectrum estimation is seriously distorted due to the loss of phase information. Therefore, we transform the network to run in time domain and gain 0.42 improvement on PESQ and 0.03 improvement on STOI. In addition, we integrate dilated convolution into CRN architecture, and adopt three different types of bottleneck modules, namel

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Chim, Seyha, Jin-Gu Lee, and Ho-Hyun Park. "Dilated Skip Convolution for Facial Landmark Detection." Sensors 19, no. 24 (2019): 5350. http://dx.doi.org/10.3390/s19245350.

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Facial landmark detection has gained enormous interest for face-related applications due to its success in facial analysis tasks such as facial recognition, cartoon generation, face tracking and facial expression analysis. Many studies have been proposed and implemented to deal with the challenging problems of localizing facial landmarks from given images, including large appearance variations and partial occlusion. Studies have differed in the way they use the facial appearances and shape information of input images. In our work, we consider facial information within both global and local con

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Zhao, Feng, Junjie Zhang, Zhe Meng, and Hanqiang Liu. "Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification." Remote Sensing 13, no. 17 (2021): 3396. http://dx.doi.org/10.3390/rs13173396.

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Recently, with the extensive application of deep learning techniques in the hyperspectral image (HSI) field, particularly convolutional neural network (CNN), the research of HSI classification has stepped into a new stage. To avoid the problem that the receptive field of naive convolution is small, the dilated convolution is introduced into the field of HSI classification. However, the dilated convolution usually generates blind spots in the receptive field, resulting in discontinuous spatial information obtained. In order to solve the above problem, a densely connected pyramidal dilated convo

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Ülkü, İrem. "Effect of dilation rate on Nested U-Net model performance in remote sensing." Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 67, no. 1 (2024): 27–42. https://doi.org/10.33769/aupse.1498035.

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High spatial resolution remote sensing images contain substantial detailed multi-scale objects. Convolutional neural networks (CNNs) are not efficient enough for detecting these objects of varying sizes. Among the multitude of CNN approaches, the Nested U-Net (UNet++) model shows great potential to capture more complex details by progressively enriching highresolution feature maps. However, there is more room for improving the Nested U-Net architecture by increasing its ability to detect multi-scale objects. The nested blocks used in this architecture rely on standard convolutional layers, whi

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Tang, Jingfan, Meijia Zhou, Pengfei Li, Min Zhang, and Ming Jiang. "Crowd Counting Based on Multiresolution Density Map and Parallel Dilated Convolution." Scientific Programming 2021 (January 20, 2021): 1–10. http://dx.doi.org/10.1155/2021/8831458.

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The current crowd counting tasks rely on a fully convolutional network to generate a density map that can achieve good performance. However, due to the crowd occlusion and perspective distortion in the image, the directly generated density map usually neglects the scale information and spatial contact information. To solve it, we proposed MDPDNet (Multiresolution Density maps and Parallel Dilated convolutions’ Network) to reduce the influence of occlusion and distortion on crowd estimation. This network is composed of two modules: (1) the parallel dilated convolution module (PDM) that combines

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Ma, Tian, Xinlei Zhou, Jiayi Yang, et al. "Dental Lesion Segmentation Using an Improved ICNet Network with Attention." Micromachines 13, no. 11 (2022): 1920. http://dx.doi.org/10.3390/mi13111920.

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Precise segmentation of tooth lesions is critical to creation of an intelligent tooth lesion detection system. As a solution to the problem that tooth lesions are similar to normal tooth tissues and difficult to segment, an improved segmentation method of the image cascade network (ICNet) network is proposed to segment various lesion types, such as calculus, gingivitis, and tartar. First, the ICNet network model is used to achieve real-time segmentation of lesions. Second, the Convolutional Block Attention Module (CBAM) is integrated into the ICNet network structure, and large-size convolution

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Song, Zhendong, Yupeng Ma, Fang Tan, and Xiaoyi Feng. "Hybrid Dilated and Recursive Recurrent Convolution Network for Time-Domain Speech Enhancement." Applied Sciences 12, no. 7 (2022): 3461. http://dx.doi.org/10.3390/app12073461.

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In this paper, we propose a fully convolutional neural network based on recursive recurrent convolution for monaural speech enhancement in the time domain. The proposed network is an encoder-decoder structure using a series of hybrid dilated modules (HDM). The encoder creates low-dimensional features of a noisy input frame. In the HDM, the dilated convolution is used to expand the receptive field of the network model. In contrast, the standard convolution is used to make up for the under-utilized local information of the dilated convolution. The decoder is used to reconstruct enhanced frames.

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Viriyasaranon, Thanaporn, Seung-Hoon Chae, and Jang-Hwan Choi. "MFA-net: Object detection for complex X-ray cargo and baggage security imagery." PLOS ONE 17, no. 9 (2022): e0272961. http://dx.doi.org/10.1371/journal.pone.0272961.

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Deep convolutional networks have been developed to detect prohibited items for automated inspection of X-ray screening systems in the transport security system. To our knowledge, the existing frameworks were developed to recognize threats using only baggage security X-ray scans. Therefore, the detection accuracy in other domains of security X-ray scans, such as cargo X-ray scans, cannot be ensured. We propose an object detection method for efficiently detecting contraband items in both cargo and baggage for X-ray security scans. The proposed network, MFA-net, consists of three plug-and-play mo

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Zhang, Jianming, Chaoquan Lu, Jin Wang, Lei Wang, and Xiao-Guang Yue. "Concrete Cracks Detection Based on FCN with Dilated Convolution." Applied Sciences 9, no. 13 (2019): 2686. http://dx.doi.org/10.3390/app9132686.

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In civil engineering, the stability of concrete is of great significance to safety of people’s life and property, so it is necessary to detect concrete damage effectively. In this paper, we treat crack detection on concrete surface as a semantic segmentation task that distinguishes background from crack at the pixel level. Inspired by Fully Convolutional Networks (FCN), we propose a full convolution network based on dilated convolution for concrete crack detection, which consists of an encoder and a decoder. Specifically, we first used the residual network to extract the feature maps of the in

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Rozprawy doktorskie na temat "Convolution dilatée"

Khalfaoui, Hassani Ismail. "Convolution dilatée avec espacements apprenables." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSES017.

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Dans cette thèse, nous avons développé et étudié la méthode de convolution dilatée avec espacements apprenables (Dilated Convolution with Learnable Spacings en anglais, qu'on abrégera par le sigle DCLS). La méthode DCLS peut être considérée comme une extension de la méthode de convolution dilatée standard, mais dans laquelle les positions des poids d'un réseau de neurones sont apprises grâce à l'algorithme de rétropropagation du gradient, et ce, à l'aide d'une technique d'interpolation. Par suite, nous avons démontré empiriquement l'efficacité de la méthode DCLS en fournissant des preuves conc

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Börjesson, Lukas. "Forecasting Financial Time Series through Causal and Dilated Convolutional Neural Networks." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-167331.

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In this paper, predictions of future price movements of a major American stock index was made by analysing past movements of the same and other correlated indices. A model that has shown very good results in speech recognition was modified to suit the analysis of financial data and was then compared to a base model, restricted by assumptions made for an efficient market. The performance of any model, that is trained by looking at past observations, is heavily influenced by how the division of the data into train, validation and test sets is made. This is further exaggerated by the temporal str

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Highlander, Tyler Clayton. "Conditional Dilated Attention Tracking Model - C-DATM." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1564652134758139.

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Yeh, Pin-Yi, and 葉品儀. "Multi-Scale Neural Network with Dilated Convolutions for Image Deblurring." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/vgs5cw.

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碩士 國立臺灣科技大學 資訊工程系 107 Several deep learning-based approaches are successful in single image deblurring, particularly, convolutional neural networks (CNN). Unlike traditional methods which try to estimate the blur kernel to extract the latent sharp image, CNN-based methods can directly find the mapping from the blurry input image to the latent sharp image. CNN usually has many layers to represent complex spatial relationships, and down-sampling layers are used to reduce the number of parameters (e.g., encoder-decoder architecture). However, down-sampling causes some spatial informat

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Liu, Chien-Chung, and 劉建忠. "Improved Image Super Resolution Technology Based on Dilated Convolutional Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/w6cn2k.

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碩士 國立臺中科技大學 資訊工程系碩士班 106 Image super resolution is wide application in image processing and computer vision. Because original super resolution image can’t be irreversible and it have distorted pixel values after the image is enlarged are challenging subjects. This paper proposed two architectures which is using convolutional neural network architecture of deep learning to carry out image super resolution. They estimate pixels of super resolution image by neurons of convolutional neural network. The first architecture is reduced dilated convolutional neural network. It reduces dilat

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Części książek na temat "Convolution dilatée"

Guru Pradeep Reddy, T., Kandiraju Sai Ashritha, T. M. Prajwala, et al. "Retinal-Layer Segmentation Using Dilated Convolutions." In Proceedings of 3rd International Conference on Computer Vision and Image Processing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9088-4_24.

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Zhang, Jinglu, Yinyu Nie, Yao Lyu, et al. "Symmetric Dilated Convolution for Surgical Gesture Recognition." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59716-0_39.

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Gupta, Sachin, Priya Goyal, Bhuman Vyas, Mohammad Shabaz, Suchitra Bala, and Aws Zuhair Sameen. "Dilated convolution model for lightweight neural network." In Next Generation Computing and Information Systems. CRC Press, 2024. http://dx.doi.org/10.1201/9781003466383-20.

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Hu, Haigen, Chenghan Yu, Qianwei Zhou, Qiu Guan, and Qi Chen. "SAMDConv: Spatially Adaptive Multi-scale Dilated Convolution." In Pattern Recognition and Computer Vision. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8543-2_37.

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Shen, Falong, and Gang Zeng. "Gaussian Dilated Convolution for Semantic Image Segmentation." In Advances in Multimedia Information Processing – PCM 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00776-8_30.

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Sun, Wei, Xijie Zhou, Xiaorui Zhang, and Xiaozheng He. "A Lightweight Neural Network Combining Dilated Convolution and Depthwise Separable Convolution." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48513-9_17.

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Zhang, Jing, Haiguang Li, Chao Zhang, Yangbiao Wu, and Guiyi Liu. "Bearing Remaining Life Prediction Based on Temporal Convolutional Networks with Hybrid Dilated Convolutions." In Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023). Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-49421-5_27.

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Chen, Zhaokang, and Bertram E. Shi. "Appearance-Based Gaze Estimation Using Dilated-Convolutions." In Computer Vision – ACCV 2018. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20876-9_20.

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Maraci, Mohammad Ali, Weidi Xie, and J. Alison Noble. "Can Dilated Convolutions Capture Ultrasound Video Dynamics?" In Machine Learning in Medical Imaging. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00919-9_14.

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Xin, Bin, Yaning Yang, Dongqing Wei, and Shaoliang Peng. "CFCN: A Multi-scale Fully Convolutional Network with Dilated Convolution for Nuclei Classification and Localization." In Bioinformatics Research and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91415-8_27.

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Streszczenia konferencji na temat "Convolution dilatée"

Zhang, Wang, Subhro Das, Lam M. Nguyen, and Luca Daniel. "Dilated Convolution for Time Series Learning." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10887837.

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Munir, Mustafa, Md Mostafijur Rahman, and Radu Marculescu. "RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00805.

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Tian, Luhao, De Li, and Xun Jin. "Image Tampering Detection Method Based on Dilated Convolution." In 2024 13th International Conference of Information and Communication Technology (ICTech). IEEE, 2024. https://doi.org/10.1109/ictech63197.2024.00093.

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Wang, chun, and fei Tang. "Multiscale dilated convolution TCN lip reading recognition model research." In Fourth International Conference on Electronics Technology and Artificial Intelligence (ETAI 2025), edited by Shaohua Luo and Akash Saxena. SPIE, 2025. https://doi.org/10.1117/12.3068623.

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Chen, Zhanchi, Caijuan He, Chi Chen, Xuejiao Liao, Xun Liu, and Tianyao Liang. "Dual Branch Network by Fusing Standard Convolution and Dilated Convolution for Plant Leaf Diseases Classification." In 2025 IEEE 7th International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2025. https://doi.org/10.1109/cisce65916.2025.11065463.

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Fan, Wuhui, Zihao Yang, Quan Liu, and Kun Chen. "A Lightweight Dilated Convolution- Channel Attention Network for Fault Detection." In 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT). IEEE, 2025. https://doi.org/10.1109/ainit65432.2025.11035022.

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Stringhini, Romulo M., Thiago L. T. da Silveira, and Claudio R. Jung. "Spherically-Weighted Horizontally Dilated Convolutions for Omnidirectional Image Processing." In 2024 37th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2024. http://dx.doi.org/10.1109/sibgrapi62404.2024.10716273.

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Li, Zhen, Zhibiao Zhao, Yulang He, Yan Shi, and Qi Zhou. "D-CBAMFi: A Lightweight Multi-Scale Fusion Network Based on Depthwise Separable Convolution and Dilated Convolution." In 2024 5th International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI). IEEE, 2024. https://doi.org/10.1109/ichci63580.2024.10807997.

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Liu, Jen-Yu, and Yi-Hsuan Yang. "Dilated Convolution with Dilated GRU for Music Source Separation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/655.

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Stacked dilated convolutions used in Wavenet have been shown effective for generating high-quality audios. By replacing pooling/striding with dilation in convolution layers, they can preserve high-resolution information and still reach distant locations. Producing high-resolution predictions is also crucial in music source separation, whose goal is to separate different sound sources while maintain the quality of the separated sounds. Therefore, in this paper, we use stacked dilated convolutions as the backbone for music source separation. Although stacked dilated convolutions can reach wider

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Wu, Lin (Yuanbo), Deyin Liu, Xiaojie Guo, Richang Hong, Liangchen Liu, and Rui Zhang. "Multi-scale Spatial Representation Learning via Recursive Hermite Polynomial Networks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/204.

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Multi-scale representation learning aims to leverage diverse features from different layers of Convolutional Neural Networks (CNNs) for boosting the feature robustness to scale variance. For dense prediction tasks, two key properties should be satisfied: the high spatial variance across convolutional layers, and the sub-scale granularity inside a convolutional layer for fine-grained features. To pursue the two properties, this paper proposes Recursive Hermite Polynomial Networks (RHP-Nets for short). The proposed RHP-Nets consist of two major components: 1) a dilated convolution to maintain th

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