Article
Computer Science, Artificial Intelligence
Pengqian Li, Xiaofen Xing, Xiangmin Xu, Bolun Cai, Jun Cheng
Summary: This paper presents a biologically-inspired saliency prediction method that imitates two main characteristics of the human perception process: focalization and orienting. The proposed ACNet consists of two modules, a concentrated module (CM) and a parallel attention module (PAM), which together form the core component ACBlock for progressively refining saliency estimation. Experimental results show that ACNet outperforms state-of-the-art models without prior knowledge or post-processing.
Article
Computer Science, Information Systems
Zhaojian Yao, Luping Wang
Summary: This research focuses on the use of boundary information in saliency detection and proposes a new network structure to generate more accurate saliency maps by learning boundary features. Experimental results demonstrate that the proposed method outperforms 15 state-of-the-art methods on benchmark datasets.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
Multidisciplinary Sciences
Sajib Saha, Janardhan Vignarajan, Shaun Frost
Summary: This paper presents a computationally efficient and memory efficient CNN-based system for automated detection of glaucoma. The system achieves high accuracy and speed while minimizing resource requirements. It performs well in classifying glaucomatous and non-glaucomatous images, making it suitable for integration into portable fundus cameras.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Artificial Intelligence
Dongjing Shan, Xiongwei Zhang, Tieyong Cao, Limin Wang, Chao Zhang
Summary: In this article, a three-stage hierarchical neural network is proposed for saliency detection, combining fast R-CNN, self-attention mechanism, and global regression model. Experimental results demonstrate excellent performance on several benchmark datasets and comparisons with 12 previous methods were conducted.
IEEE INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Zeynab Barzegar, Mansour Jamzad
Summary: Automatic brain abnormality detection is a major challenge in medical image processing. This study presents an efficient and automated algorithm for brain tumour segmentation based on the symmetry of brain structures. The algorithm uses symmetry to determine lesion differences and extract cancerous regions, improving speed and accuracy. Experimental results show performance indices close to manual lesion demarcation accuracy.
IET COMPUTER VISION
(2021)
Article
Geochemistry & Geophysics
Hagar S. Elsayed, Omar M. Saad, M. Sami Soliman, Yangkang Chen, Hassan A. Youness
Summary: We propose a novel deep learning method, attention-based fully convolutional dense network (FCDNet), for automatic earthquake detection. The FCDNet, with the addition of spatial attention mechanism and utilization of time-frequency features, achieves higher accuracy and robustness in earthquake detection. Testing on multiple datasets demonstrates the effectiveness and generalization ability of the attention-based FCDNet.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Automation & Control Systems
Jinxia Zhang, Yu Shen, Jiacheng Jiang, Shixiong Fang, Liping Chen, Tingting Yan, Zuoyong Li, Kanjian Zhang, Haikun Wei, Weili Guo
Summary: In this article, a global pairwise similarity and concatenated saliency guided neural network is proposed for automatically detecting defects in solar cells using electroluminescence images. The proposed method significantly outperforms baseline models and demonstrates the effectiveness of the global similarity module and the concatenated saliency module in defect detection.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Green & Sustainable Science & Technology
Yimang Li, Jingwei Shang, Meng Yan, Bei Ding, Jiacheng Zhong
Summary: Fire disasters cause significant damage and early detection is crucial. Convolution neural networks (CNNs) have shown promise in fire detection but their computational cost and model size limit deployment. This paper presents a real-time indoor fire-detection and -localization system that addresses these challenges and achieves accurate and efficient results.
Article
Computer Science, Information Systems
Muwei Jian, Jiaojin Wang, Hui Yu, Gai-Ge Wang
Summary: In this paper, we propose an efficient video saliency-detection model that integrates object-proposal with attention networks to capture salient objects and human attention areas in dynamic video scenes. Experimental results show that our framework outperforms existing deep models in video saliency detection.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Xiaowei Chen, Qing Zhang, Liqian Zhang
Summary: The proposed edge-aware salient object detection network utilizes high-level semantic information to assist feature selection and locates salient objects by extracting multi-scale features and emphasizing important feature channels. It adopts a context guidance strategy to fuse high-level and low-level information and supervises the generation of low-level edge information.
IMAGE AND VISION COMPUTING
(2021)
Article
Environmental Sciences
Mengmeng Yin, Zhibo Chen, Chengjian Zhang
Summary: This article proposes a hybrid CNN-Transformer architecture named CTCANet for high-resolution bi-temporal remote sensing image change detection. CTCANet combines the strengths of convolutional networks, transformers, and attention mechanisms to obtain high-level feature representations and accurately locate small targets. Experimental results demonstrate that CTCANet outperforms recent state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Binyi Wu, Bernd Waschneck, Christian Georg Mayr
Summary: The proposed double-stage ST activation quantization method utilizes attention mechanism and learned thresholds to distinguish objects, supporting both binarization and multi-bit quantization, achieving state-of-the-art results.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Ronghua Shang, Wenzheng Li, Songling Zhu, Licheng Jiao, Yangyang Li
Summary: This paper proposes a multi-teacher knowledge distillation method based on the joint guidance of probe and adaptive corrector (GPAC). It addresses the issues in current multi-teacher KD algorithms, such as passive acquisition of knowledge and identical guiding schemes. The experimental results show that the proposed method achieves higher classification accuracy.
Article
Computer Science, Interdisciplinary Applications
Bao-Luo Li, Yu Qi, Jian-Sheng Fan, Yu-Fei Liu, Cheng Liu
Summary: Crack identification is crucial for preventive maintenance of asphalt pavement. This paper describes a fusion model based on the YOLO v5 that combines grid-based classification and box-based detection, achieving high accuracy and efficiency. The proposed NMS-ARS algorithm improves crack topology detection through postprocessing. Experimental results demonstrate the effective automatic crack identification for asphalt pavement.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Hemin Ali Ali, Younghak Shin, Johannes Solhusvik, Jacob Bergsland, Lars Aabakken, Ilangko Balasingham
Summary: The proposed method utilizes 2D Gaussian masks for real-time polyp detection, achieving state-of-the-art results on two polyp datasets. This approach effectively detects different types of polyps and reduces false positives.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Geochemistry & Geophysics
Ali Bahri, Sina Ghofrani Majelan, Sina Mohammadi, Mehrdad Noori, Karim Mohammadi
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2020)
Article
Geography, Physical
Sina Mohammadi, Mariana Belgiu, Alfred Stein
Summary: This study proposes a method to improve the classification performance of deep learning models on unseen data in crop satellite image time series by introducing intermediate supervision methods. By supervising the intermediate layers of a 3D convolutional neural network, feature discrimination and clustering can be enhanced, thereby improving network performance. The experiments show that this method outperforms existing methods in identifying corn, soybean, and other crops. Therefore, proper supervision of deep neural networks plays a significant role in improving crop mapping performance.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Sina Mohammadi, Sina Ghofrani Majelan, Shahriar B. Shokouhi
2019 9TH INTERNATIONAL CONFERENCE ON COMPUTER AND KNOWLEDGE ENGINEERING (ICCKE 2019)
(2019)
Article
Computer Science, Artificial Intelligence
Guang-Hai Liu, Zuo-Yong Li, Jing-Yu Yang, David Zhang
Summary: This article introduces a novel image retrieval method that improves retrieval performance by using sublimated deep features. The method incorporates orientation-selective features and color perceptual features, effectively mimicking these mechanisms to provide a more discriminating representation.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Fengguang Peng, Zihan Ding, Ziming Chen, Gang Wang, Tianrui Hui, Si Liu, Hang Shi
Summary: RGB-Thermal (RGB-T) semantic segmentation is an emerging task that aims to improve the robustness of segmentation methods under extreme imaging conditions by using thermal infrared modality. The challenges of foreground-background distinguishment and complementary information mining are addressed by proposing a cross modulation process with two collaborative components. Experimental results show that the proposed method achieves state-of-the-art performances on current RGB-T segmentation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Baihong Han, Xiaoyan Jiang, Zhijun Fang, Hamido Fujita, Yongbin Gao
Summary: This paper proposes a novel automatic prompt generation method called F-SCP, which focuses on generating accurate prompts for low-accuracy classes and similar classes. Experimental results show that our approach outperforms state-of-the-art methods on six multi-domain datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Huikai Liu, Ao Zhang, Wenqian Zhu, Bin Fu, Bingjian Ding, Shengwu Xiong
Summary: Adverse weather conditions present challenges for computer vision tasks, and image de-weathering is an important component of image restoration. This paper proposes a multi-patch skip-forward structure and a Residual Deformable Convolutional module to improve feature extraction and pixel-wise reconstruction.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schonlieb, Matthew Thorpe, Konstantinos C. Zygalakis
Summary: The transportation LP distance (TLP) is a generalization of the Wasserstein WP distance that can be applied directly to color or multi-channelled images, as well as multivariate time-series. TLP interprets signals as functions, while WP interprets signals as measures. Although both distances are powerful tools in modeling data with spatial or temporal perturbations, their computational cost can be prohibitively high for moderate pattern recognition tasks. The linear Wasserstein distance offers a method for projecting signals into a Euclidean space, and in this study, we propose linear versions of the TLP distance (LTLP) that show significant improvement over the linear WP distance in signal processing tasks while being several orders of magnitude faster to compute than the TLP distance.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Haitao Tian, Shiru Qu, Pierre Payeur
Summary: This paper proposes a method of target-dependent classifier, which optimizes the joint hypothesis of domain adaptation into a target-dependent hypothesis that better fits with the target domain clusters through an unsupervised fine-tuning strategy and the concept of meta-learning. Experimental results demonstrate that this method outperforms existing techniques in synthetic-to-real adaptation and cross-city adaptation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Qingsen Yan, Axi Niu, Chaoqun Wang, Wei Dong, Marcin Wozniak, Yanning Zhang
Summary: Deep learning-based methods have achieved remarkable results in the field of super-resolution. However, the limitation of paired training image sets has led researchers to explore self-supervised learning. However, the assumption of inaccurate downscaling kernel functions often leads to degraded results. To address this issue, this paper introduces KGSR, a kernel-guided network that trains both upscaling and downscaling networks to generate high-quality high-resolution images even without knowing the actual downscaling process.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Yifan Chen, Xuelong Li
Summary: Gait recognition is a popular technology for identification due to its ability to capture gait features over long distances without cooperation. However, current methods face challenges as they use a single network to extract both temporal and spatial features. To solve this problem, we propose a two-branch network that focuses on spatial and temporal feature extraction separately. By combining these features, we can effectively learn the spatio-temporal information of gait sequences.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Wei Shi, Wentao Zhang, Wei-shi Zheng, Ruixuan Wang
Summary: This article proposes a simple yet effective visualization framework called PAMI, which does not require detailed model structure and parameters to obtain visualization results. It can be applied to various prediction tasks with different model backbones and input formats.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Xiaobo Hu, Jianbo Su, Jun Zhang
Summary: This paper reviews the latest technologies in pattern recognition, highlighting their instabilities and failures in practical applications. From a control perspective, the significance of disturbance rejection in pattern recognition is discussed, and the existing problems are summarized. Finally, potential solutions related to the application of compensation on features are discussed to emphasize future research directions.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Andres Felipe Posada-Moreno, Nikita Surya, Sebastian Trimpe
Summary: Convolutional neural networks are widely used in critical systems, and explainable artificial intelligence has proposed methods for generating high-level explanations. However, these methods lack the ability to determine the location of concepts. To address this, we propose a novel method for automatic concept extraction and localization based on pixel-wise aggregations, and validate it using synthetic datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Peng Bao, Jianian Li, Rong Yan, Zhongyi Liu
Summary: In this paper, a novel Dynamic Graph Contrastive Learning framework, DyGCL, is proposed to capture the temporal consistency in dynamic graphs and achieve good performance in node representation learning.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Kristian Schultz, Saptarshi Bej, Waldemar Hahn, Markus Wolfien, Prashant Srivastava, Olaf Wolkenhauer
Summary: Research indicates that deep generative models perform poorly compared to linear interpolation-based methods for synthetic data generation on small, imbalanced tabular datasets. To address this, a new approach called ConvGeN, combining convex space learning with deep generative models, has been proposed. ConvGeN improves imbalanced classification on small datasets while remaining competitive with existing linear interpolation methods.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Khondaker Tasrif Noor, Antonio Robles-Kelly
Summary: In this paper, the authors propose H-CapsNet, a capsule network designed for hierarchical image classification. The network effectively captures hierarchical relationships using dedicated capsules for each class hierarchy. A modified hinge loss is utilized to enforce consistency among the involved hierarchies. Additionally, a strategy for dynamically adjusting training parameters is presented to achieve better balance between the class hierarchies. Experimental results demonstrate that H-CapsNet outperforms competing hierarchical classification networks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Lei Liu, Guorun Li, Yuefeng Du, Xiaoyu Li, Xiuheng Wu, Zhi Qiao, Tianyi Wang
Summary: This study proposes a new agricultural image segmentation model called CS-Net, which uses Simple-Attention Block and Simpleformer to improve accuracy and inference speed, and addresses the issue of performance collapse of Transformers in agricultural image processing.
PATTERN RECOGNITION
(2024)