Relevant bibliographies by topics / Dilated convolution

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Dilated convolution'

Author: Grafiati
Published: 24 August 2024
Last updated: 31 July 2025

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Journal articles on the topic "Dilated convolution"

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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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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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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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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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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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Yan, Yang, Liu Yang, and Wenbo Huang. "Fundus-DANet: Dilated Convolution and Fusion Attention Mechanism for Multilabel Retinal Fundus Image Classification." Applied Sciences 14, no. 18 (2024): 8446. http://dx.doi.org/10.3390/app14188446.

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The difficulty of classifying retinal fundus images with one or more illnesses present or missing is known as fundus multi-lesion classification. The challenges faced by current approaches include the inability to extract comparable morphological features from images of different lesions and the inability to resolve the issue of the same lesion, which presents significant feature variances due to grading disparities. This paper proposes a multi-disease recognition network model, Fundus-DANet, based on the dilated convolution. It has two sub-modules to address the aforementioned issues: the int
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Cao, Ruifen, Xi Pei, Ning Ge, and Chunhou Zheng. "Clinical Target Volume Auto-Segmentation of Esophageal Cancer for Radiotherapy After Radical Surgery Based on Deep Learning." Technology in Cancer Research & Treatment 20 (January 1, 2021): 153303382110342. http://dx.doi.org/10.1177/15330338211034284.

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Radiotherapy plays an important role in controlling the local recurrence of esophageal cancer after radical surgery. Segmentation of the clinical target volume is a key step in radiotherapy treatment planning, but it is time-consuming and operator-dependent. This paper introduces a deep dilated convolutional U-network to achieve fast and accurate clinical target volume auto-segmentation of esophageal cancer after radical surgery. The deep dilated convolutional U-network, which integrates the advantages of dilated convolution and the U-network, is an end-to-end architecture that enables rapid t
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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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Wang, Ran, Ruyu Shi, Xiong Hu, and Changqing Shen. "Remaining Useful Life Prediction of Rolling Bearings Based on Multiscale Convolutional Neural Network with Integrated Dilated Convolution Blocks." Shock and Vibration 2021 (January 25, 2021): 1–11. http://dx.doi.org/10.1155/2021/6616861.

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Remaining useful life (RUL) prediction is necessary for guaranteeing machinery’s safe operation. Among deep learning architectures, convolutional neural network (CNN) has shown achievements in RUL prediction because of its strong ability in representation learning. Features from different receptive fields extracted by different sizes of convolution kernels can provide complete information for prognosis. The single size convolution kernel in traditional CNN is difficult to learn comprehensive information from complex signals. Besides, the ability to learn local and global features synchronously
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Dissertations / Theses on the topic "Dilated convolution"

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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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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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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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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<br>國立臺灣科技大學<br>資訊工程系<br>107<br>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<br>國立臺中科技大學<br>資訊工程系碩士班<br>106<br>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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Book chapters on the topic "Dilated convolution"

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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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Qian, Likuan, Yuanfeng Lian, Qian Wei, Shuangyuan Wu, and Jianbin Zhang. "ODCN: Optimized Dilated Convolution Network for 3D Shape Segmentation." In Pattern Recognition and Computer Vision. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31726-3_32.

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Wu, Yan, Wei Jiang, Jiqian Li, and Tao Yang. "Speeding Up Dilated Convolution Based Pedestrian Detection with Tensor Decomposition." In Intelligent Computing Methodologies. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63315-2_11.

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Pan, Xiaoying, Dong Dai, Hongyu Wang, Xingxing Liu, and Weidong Bai. "Nasopharyngeal Organ Segmentation Algorithm Based on Dilated Convolution Feature Pyramid." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6963-7_4.

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Wei, Xinlei, Yingji Liu, Wei Zhou, Haiying Xia, Daxin Tian, and Ruifen Cheng. "Traffic Crowd Congested Scene Recognition Based on Dilated Convolution Network." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1160-5_12.

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Tureckova, Alzbeta, and Antonio J. Rodríguez-Sánchez. "ISLES Challenge: U-Shaped Convolution Neural Network with Dilated Convolution for 3D Stroke Lesion Segmentation." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11723-8_32.

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Conference papers on the topic "Dilated convolution"

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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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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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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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Zhang, Cong, Jiabing Wang, and Liang Zhao. "Intelligent Building Damage Detection via Integrated Spatial Attention and Dilated Convolution." In 2025 8th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). IEEE, 2025. https://doi.org/10.1109/aemcse65292.2025.11042808.

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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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Ouyang, Yingfeng, Bingo Wing-Kuen Ling, and Weizhi Guo. "Classification of Cognitive EEGs via Attention-Based Multi-Scale Dilated Convolution Network." In 2024 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA). IEEE, 2024. http://dx.doi.org/10.1109/ispce-asia64773.2024.10756217.

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Gao, Ruixin, Yisong Ma, Hongxing Xu, et al. "High-precision steel plate corrosion rate monitoring model based on dilated convolution." In Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024), edited by Xiangjie Kong and Xingjian Wang. SPIE, 2024. http://dx.doi.org/10.1117/12.3049767.

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