24 papers · ranked by Valyu relevance
Lois Orosa, Skanda Koppula, Yaman Umuroglu, Konstantinos Kanellopoulos + 4 more
'Konstantinos Kanellopoulos' 'Juan Gómez-Luna' 'Michaela Blott' 'Kees Vissers' 'Onur Mutlu'] Abstract—Dilated and transposed convolutions are widely used in modern convolutional neural networks (CNNs). These kernels are used extensively during CNN training and inference of applications such as image segmentation and…
Cong Xu, Xuqi Wang, Shanwen Zhang
Accurate and rapid identification of apple leaf diseases is the basis for preventing and treating apple diseases. However, it is challenging to identify apple leaf diseases due to their various symptoms, different colors, irregular shapes, uneven sizes, and complex backgrounds. To reduce computational cost and improve…
Tao Wang, Zenghui Ding, Xianjun Yang, Yanyan Chen + 4 more
'Xiaoming Kong' 'Yining Sun' 'Ivan Miguel Pires'] Mild cognitive impairment (MCI) is a precursor to neurodegenerative diseases such as Alzheimer’s disease, and an early diagnosis and intervention can delay its progression. However, the brain MRI images of MCI patients have small changes and blurry shapes. At the same…
Xiajiong Shen, Kunying Meng, Lei Zhang, Xianyu Zuo
The neural network method can obtain a higher precision of radar echo extrapolation than the traditional method. However, its application in radar echo extrapolation is still in the initial stage of exploration, and there is still much room for improvement in the extrapolation accuracy. To improve the utilization of…
Mohd Jawed Khan, Pankaj Pratap Singh, Biswajeet Pradhan, Abdullah Alamri + 6 more
'Abdullah Alamri' 'Chang-Wook Lee' 'Giovanni Pau' 'Zhiheng Li' 'Hailong Zhu' 'Yilong Ren' 'Jiyuan Tan'] Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing…
Shanqin Wang, Miao Zhang, Mengjun Miao
Aiming at the problems of traditional image super-resolution reconstruction algorithms in the image reconstruction process, such as small receptive field, insufficient multi-scale feature extraction, and easy loss of image feature information, a super-resolution reconstruction algorithm of multi-scale dilated…
Mustafa Munir, Md Mostafijur Rahman, Radu Mărculescu
Vision transformers (ViTs) have dominated computer vision in recent years. However, ViTs are computationally expensive and not well suited for mobile devices; this led to the prevalence of convolutional neural network (CNN) and ViT-based hybrid models for mobile vision applications. Recently, Vision GNN (ViG) and CNN…
Fengdan Hu, Haigen Hu, Hui Xu, Jinshan Xu + 2 more
Owing to the variable shapes, large size difference, uneven grayscale, and dense distribution among biological cells in an image, it is very difficult to accurately detect and segment cells. Especially, it is a serious challenge for some microscope imaging devices with limited resources owing to a large number of…
Tooba Rashid, Muhammad Sultan Zia, Najam-ur-Rehman, Talha Meraj + 5 more
The emergency department of hospitals receives a massive number of patients with wrist fracture. For the clinical diagnosis of a suspected fracture, X-ray imaging is the major screening tool. A wrist fracture is a significant global health concern for children, adolescents, and the elderly. A missed diagnosis of wrist…
Chenchen Liu, Haoyue Guo, Alberto Marchisio
This study aims to explore a data-driven cultural background fusion method to improve the accuracy of environmental art image classification. A novel Dual Kernel Squeeze and Excitation Network (DKSE-Net) model is proposed for the complex cultural background and diverse visual representation in environmental art images.…
Zhiyi Zhang, Pengfei Zhang, Zhuopin Xu, Wang Qi
—Convolutional neural networks necessitate good algorithms to reduce complexity, and sufficient utilization of parallel processors for acceleration. Within convolutional layers, there are three types of operators: convolution used in forward propagation, deconvolution and dilated-convolution utilized in backward…
Mengxuan Li, Peng Peng, Min Wang, Hongwei Wang
—Fault detection and diagnosis is significant for reducing maintenance costs and improving health and safety in chemical processes. Convolution neural network (CNN) is a popular deep learning algorithm with many successful applications in chemical fault detection and diagnosis tasks. However, convolution layers in CNN…
Linwei Chen, Lin Gu, Ying Fu
Dilated convolution, which expands the receptive field by inserting gaps between its consecutive elements, is widely employed in computer vision. In this study, we propose three strategies to improve individual phases of dilated convolution from the perspective of spectrum analysis. Departing from the conventional…
Yufei Zeng, Yanxiong Li, Zhenfeng Zhou, Ruiqi Wang + 1 more
—Domestic activities classification (DAC) from audio recordings aims at classifying audio recordings into predefined categories of domestic activities, which is an effective way for estimation of daily activities performed in home environment. In this paper, we propose a method for DAC from audio recordings using a…
Samuel Olowofila, Oluwatosin Oluwadare
The spatial organization of chromatin is fundamental to gene regulation and essential for proper cellular function. The Hi-C technique remains the leading method for unraveling 3D genome structures, but the limited availability of high-resolution Hi-C data poses significant challenges for comprehensive analysis. Deep…
P. N. Karthikayan, Yoga Sri Varshan V, Hitesh Gupta Kattamuri, Umarani Jayaraman
for Fundus Disease Classification Authors: ['P. N. Karthikayan' 'Yoga Sri Varshan V' 'Hitesh Gupta Kattamuri' 'Umarani Jayaraman'] This paper presents dilated Residual Network (ResNet) models for disease classification from retinal fundus images. Dilated convolution filters are used to replace normal convolution…
Jiacheng Leng, Jiating Yu, Ling-Yun Wu
Differential graph inference is a critical analytical technique that enables researchers to accurately identify the variables and their interactions that change under different conditions. By comparing two conditions, researchers can gain a deeper understanding of the differences between them. Currently, the mainstream…
Yik San Cheng, Runkai Zhao, Heng Wang, Hanchuan Peng + 2 more
Accurate reconstruction of neuronal morphology from three-dimensional (3D) light microscopy is fundamental to neuroscience. Nevertheless, neuronal arbors intrinsically exhibit slender, tortuous geometries with high orientation variability, posing significant challenges for standard 3D convolutions whose static…
Authors not listed
Obtaining quantitative information about residence time behavior (i.e., the residence time distribution function) in realistic experimental systems is oftentimes experimentally challenging and numerically complex. The conventional way is to conduct very simple pulse or step tracer experiments or construct elaborate…
Robin Gutzen, Grace W Lindsay
Convolutional Neural Networks (CNNs) trained for image recognition have demonstrated remarkable conceptual similarities to the primate ventral visual pathway, but their standard feedforward architectures lack the recurrent connections that are ubiquitous in visual cortex. Such recurrence is thought to underlie…
Christian Jarvers, Heiko Neumann
Deep neural networks have been remarkably successful as models of the primate visual system. One crucial problem is that they fail to account for the strong shape-dependence of primate vision. Whereas humans base their judgements of category membership to a large extent on shape, deep networks rely much more strongly…
Varun Mannam, Scott Howard
Machine learning (ML) models based on deep convolutional neural networks have been used to significantly increase microscopy resolution, speed (signal-to-noise ratio), and data interpretation. The bottleneck in developing effective ML systems is often the need to acquire large datasets to train the neural network. This…
Linnea Evanson, Maksim Lavrov, Iakov Kharitonov, Sihao Lu + 1 more
Object recognition by natural and artificial sensory systems requires a combination of selectivity and invariance. Both natural and artificial neural networks achieve selectivity and invariance by propagating sensory information though layers of neurons organised in a functional hierarchy. Both employ computational…
Kelsey Hatzell, Yanjie Zheng
X-ray Computed Tomography (CT) is a non-invasive, non-destructive approach to imaging materials, material systems and engineered components in two- and three- dimensions. Acquisition of 3D images requires the collection of hundreds or thousands of through-thickness X-ray radiographic images from different angles. Such…