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Journal articles on the topic 'Target feature weighting'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Wang, Shaona, Yang Liu, and Linlin Li. "Sparse Weighting for Pyramid Pooling-Based SAR Image Target Recognition." Applied Sciences 12, no. 7 (2022): 3588. http://dx.doi.org/10.3390/app12073588.

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In this study, a novel feature learning method for synthetic aperture radar (SAR) image automatic target recognition is presented. It is based on spatial pyramid matching (SPM), which represents an image by concatenating the pooling feature vectors that are obtained from different resolution sub-regions. This method exploits the dependability of obtaining the weighted pooling features generated from SPM sub-regions. The dependability is determined by the residuals obtained from sparse representation. This method aims at enhancing the weights of the pooling features generated in the sub-regions

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Rahman, Ibrahim, Christopher Hollitt, and Mengjie Zhang. "Contextual-based top-down saliency feature weighting for target detection." Machine Vision and Applications 27, no. 6 (2016): 893–914. http://dx.doi.org/10.1007/s00138-016-0754-x.

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Akmal, Akmal, Rinaldi Munir, and Judhi Santoso. "Automatic Weight of Color, Texture, and Shape Features in Content-Based Image Retrieval Using Artificial Neural Network." JOIV : International Journal on Informatics Visualization 7, no. 3 (2023): 665. http://dx.doi.org/10.30630/joiv.7.3.1184.

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Image retrieval is the process of finding images in the database that are similar to the query image by measuring how close the feature values of the query image are to other images. Image retrieval is currently dominated by approaches that combine several different representations or features. The optimal weight of each feature is needed in combining the image features such as color features, texture features, and shape features. In this study, we use a multi-layer perceptron artificial neural network (MLP) method to obtain feature weights automatically and simultaneously look for optimal wei

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Ma, Yapeng, Yuhan Liu, Zongxu Pan, and Yuxin Hu. "Method of Infrared Small Moving Target Detection Based on Coarse-to-Fine Structure in Complex Scenes." Remote Sensing 15, no. 6 (2023): 1508. http://dx.doi.org/10.3390/rs15061508.

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In the combat system, infrared target detection is an important issue worthy of study. However, due to the small size of the target in the infrared image, the low signal-to-noise ratio of the image and the uncertainty of motion, how to detect the target accurately and quickly is still difficult. Therefore, in this paper, an infrared method of detecting small moving targets based on a coarse-to-fine structure (MCFS) is proposed. The algorithm mainly consists of three modules. The potential target extraction module first smoothes the image through a Laplacian filter and extracts the prior weight

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Yu, Liqun, Lu Wang, and Yongxing Xu. "Combination of Joint Representation and Adaptive Weighting for Multiple Features with Application to SAR Target Recognition." Scientific Programming 2021 (May 24, 2021): 1–9. http://dx.doi.org/10.1155/2021/9063419.

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For the synthetic aperture radar (SAR) target recognition problem, a method combining multifeature joint classification and adaptive weighting is proposed with innovations in fusion strategies. Zernike moments, nonnegative matrix factorization (NMF), and monogenic signal are employed as the feature extraction algorithms to describe the characteristics of original SAR images with three corresponding feature vectors. Based on the joint sparse representation model, the three types of features are jointly represented. For the reconstruction error vectors from different features, an adaptive weight

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Ye, Yishan, Zhenmiao Deng, Pingping Pan, and Wei He. "Range-Spread Target Detection Networks Using HRRPs." Remote Sensing 16, no. 10 (2024): 1667. http://dx.doi.org/10.3390/rs16101667.

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Range-spread target (RST) detection is an important issue for high-resolution radar (HRR). Traditional detectors relying on manually designed detection statistics have their performance limitations. Therefore, in this work, two deep learning-based detectors are proposed for RST detection using HRRPs, i.e., an NLS detector and DFCW detector. The NLS detector leverages domain knowledge from the traditional detector, treating the input HRRP as a low-level feature vector for target detection. An interpretable NLS module is designed to perform noise reduction for the input HRRP. The DFCW detector t

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Yu, Jimin, Hui Wang, Shangbo Zhou, and Shun Li. "Infrared Target Detection Based on Interval Sampling Weighting and 3D Attention Head in Complex Scenario." Applied Sciences 14, no. 1 (2023): 249. http://dx.doi.org/10.3390/app14010249.

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Thermal infrared detection technology can enable night vision and is robust in complex environments, making it highly advantageous for various fields. However, infrared images have low resolution and high noise, resulting in limited detailed information being available about the target object. This difficulty is further amplified when detecting small targets, which are prone to occlusion. In response to these challenges, we propose a model for infrared target detection designed to achieve efficient feature representation. Firstly, an interval sampling weighted (ISW) module is proposed, which s

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Töllner, Thomas, Klaus Gramann, Hermann J. Müller, and Martin Eimer. "The Anterior N1 Component as an Index of Modality Shifting." Journal of Cognitive Neuroscience 21, no. 9 (2009): 1653–69. http://dx.doi.org/10.1162/jocn.2009.21108.

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Processing of a given target is facilitated when it is defined within the same (e.g., visual–visual), compared to a different (e.g., tactile–visual), perceptual modality as on the previous trial [Spence, C., Nicholls, M., & Driver, J. The cost of expecting events in the wrong sensory modality. Perception & Psychophysics, 63, 330–336, 2001]. The present study was designed to identify electrocortical (EEG) correlates underlying this “modality shift effect.” Participants had to discriminate (via foot pedal responses) the modality of the target stimulus, visual versus tactile (Experiment 1

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Ma, Sugang, Bo Zhao, Zhiqiang Hou, Wangsheng Yu, Lei Pu, and Lei Zhang. "Robust Visual Object Tracking Based on Feature Channel Weighting and Game Theory." International Journal of Intelligent Systems 2023 (July 31, 2023): 1–19. http://dx.doi.org/10.1155/2023/6731717.

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Although the discriminative correlation filter- (DCF)-based tracker improves tracking performance, some object representation issues can still be further optimized. On the one hand, the DCF tracker’s deep convolutional features contain many noisy channels, and assigning the same weights to multiple channels cannot distinguish the importance of different channels. On the other hand, a simple weighted fusion approach cannot fully utilize the benefits of different feature types. We propose a visual object tracking algorithm based on adaptive channel weighting and feature game fusion to solve thes

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Pollmann, S., K. Mahn, B. Reimann, et al. "Selective Visual Dimension Weighting Deficit after Left Lateral Frontopolar Lesions." Journal of Cognitive Neuroscience 19, no. 3 (2007): 365–75. http://dx.doi.org/10.1162/jocn.2007.19.3.365.

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The left lateral frontopolar (LFP) cortex showed dimension change-related activation in previous event-related functional magnetic resonance imaging studies of visual singleton feature search with non-brain-lesioned participants. Here, we tested the hypothesis that LFP actively supports changes of attention from the old to the new target-defining dimension in singleton feature search. Singleton detection was selectively slowed in this task when the target-defining dimension changed in patients with left LFP lesions, compared with patients with frontomedian lesions as well as with matched contr

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Gunes, M., Gokhan Solak, Ugur Akin, Omer Erden, and Sanem Sariel. "A Generic Approach for Player Modeling Using Event-Trait Mapping and Feature Weighting." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 12, no. 1 (2021): 169–75. http://dx.doi.org/10.1609/aiide.v12i1.12886.

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There are a wide variety of studies on player modeling. However, most of these studies target a specific game or genre. In some of these works, the number of in-game actions is used as a feature for modeling a player. However, using this feature leads to a complex model, and the model may miss some high-level relations among actions. In this paper, we propose a generic player modeling method that uses action-trait mapping relations which reveal correlations among actions. Mapping from the action-space to a much smaller trait-space improves interpretability of models. Additionally, to use the d

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Tian, Chunxin, Xujun Chen, and Huxiao Li. "Small Target Detection Algorithm Based on Wavelet Domain Features and Weighted Sample Allocation Strategy." Journal of Physics: Conference Series 2637, no. 1 (2023): 012036. http://dx.doi.org/10.1088/1742-6596/2637/1/012036.

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Abstract Small target detection using UAV aerial photography has emerged as a popular research topic. The resolution of small target images is low and the background is complex. In this paper, utilizing a single-stage target detection algorithm ATSS as its foundation, we propose a small target network detection model SDW-Net that combines deformation convolution with wavelet domain feature enhancement by weighting the samples from both classification and regression tasks, and combining the strong local feature representation ability of discrete wavelet transform. Firstly, the original features

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Yu, Chenglin, and Hailong Pei. "Dynamic Weighting Translation Transfer Learning for Imbalanced Medical Image Classification." Entropy 26, no. 5 (2024): 400. http://dx.doi.org/10.3390/e26050400.

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Medical image diagnosis using deep learning has shown significant promise in clinical medicine. However, it often encounters two major difficulties in real-world applications: (1) domain shift, which invalidates the trained model on new datasets, and (2) class imbalance problems leading to model biases towards majority classes. To address these challenges, this paper proposes a transfer learning solution, named Dynamic Weighting Translation Transfer Learning (DTTL), for imbalanced medical image classification. The approach is grounded in information and entropy theory and comprises three modul

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Bing, Huang Chao, and Li Ting Hui. "Research on target tracking algorithm based on information entropy feature selection and example weighting." Journal of Physics: Conference Series 1848, no. 1 (2021): 012028. http://dx.doi.org/10.1088/1742-6596/1848/1/012028.

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Li, Wu. "The Image Feature Extraction Algorithm Based on the DWT and the Improved 2DPCA." Applied Mechanics and Materials 556-562 (May 2014): 5042–45. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5042.

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The technology of 2DPCA is the feature extraction method proposed aiming at two-dimension image based on the traditional PCA algorithm. The paper proposed a improved weighting 2DPCA algorithm, combined with the two-dimension discrete DWT to handle the image, posing the new feature abstraction method, experiment improved that the new feature abstraction method can improve the target recognition efficiently compared with the original 2DPCA algorithm.

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Yang, Zi Rong, and Zhen Zeng. "Outlier Analysis in Large Sample and High Dimensional Data Based on Feature Weighting." Applied Mechanics and Materials 571-572 (June 2014): 650–57. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.650.

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The usual method of outlier analysis is mainly analyzing the outliers according to the Anomaly Index and Variable Contribution Measurement. But in the analysis of large samples of high-dimensional data, this method is difficult. Owing to this, this paper presents a method that weight value for outliers is introduced. The features of outliers are weighted by Analytic Hierarchy Process method. Through this method, the importance of each property of outlier for data mining’s target is rationed, namely the weight number of each property is calculated. And then the correlation values, which represe

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Liu, Yan, and Kaiyu Fan. "Roller Bearing Fault Diagnosis Using Deep Transfer Learning and Adaptive Weighting." Journal of Physics: Conference Series 2467, no. 1 (2023): 012011. http://dx.doi.org/10.1088/1742-6596/2467/1/012011.

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Abstract A fault diagnosis approach for roller bearings utilizing deep transfer learning and adaptive weighting is suggested to address the issue that extra fault state samples in the target domain data of roller bearings impair the fault diagnostic accuracy. CNN-LSTM is a network model proposed by Lecun et al., which has good performance in image processing and image processing. It can effectively apply predictive local perception of time series and weight sharing of CNN, which can greatly reduce the number of networks and improve the efficiency of model learning. The method first establishes

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Tahmoresnezhad, Jafar, and Sattar Hashemi. "An Efficient yet Effective Random Partitioning and Feature Weighting Approach for Transfer Learning." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 02 (2016): 1651003. http://dx.doi.org/10.1142/s0218001416510034.

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One of the serious challenges in machine learning and pattern recognition is to transfer knowledge from related but different domains to a new unlabeled domain. Feature selection with maximum mean discrepancy (f-MMD) is a novel and effective approach to transfer knowledge from source domain (training set) into target domain (test set) where training and test sets are drawn from different distributions. However, f-MMD has serious challenges in facing datasets with large number of samples and features. Moreover, f-MMD ignores the feature-label relation in finding the reduced representation of da

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Wang, Zhan Feng, Hai Tao Su, Hong Shu Chen, Zhi Yi Hu, and Jie Liang Wang. "A Model of Target Detection in Variegated Natural Scene Based on Visual Attention." Applied Mechanics and Materials 333-335 (July 2013): 1213–18. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1213.

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Less of edge and texture information existed in traditional visual attention model in target detection due to extract only the color, brightness, directional characteristics, as well as direct sum fusion rule ignoring the difference in each characteristic. A improved model is proposed by introduced the edge, texture and the weights in fusion rules in visual computing model. First of all, DOG is employed in extracting the edge information on the basis of obtained brightness feature with multi-scale pyramid using the ITTI visual computing model; the second, the non-linear classification is proce

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Zhao, Chunhui, Jinpeng Wang, Nan Su, Yiming Yan, and Xiangwei Xing. "Low Contrast Infrared Target Detection Method Based on Residual Thermal Backbone Network and Weighting Loss Function." Remote Sensing 14, no. 1 (2022): 177. http://dx.doi.org/10.3390/rs14010177.

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Infrared (IR) target detection is an important technology in the field of remote sensing image application. The methods for IR image target detection are affected by many characteristics, such as poor texture information and low contrast. These characteristics bring great challenges to infrared target detection. To address the above problem, we propose a novel target detection method for IR images target detection in this paper. Our method is improved from two aspects: Firstly, we propose a novel residual thermal infrared network (ResTNet) as the backbone in our method, which is designed to im

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Tang, Tao, Yuting Cui, Rui Feng, and Deliang Xiang. "Vehicle Target Recognition in SAR Images with Complex Scenes Based on Mixed Attention Mechanism." Information 15, no. 3 (2024): 159. http://dx.doi.org/10.3390/info15030159.

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With the development of deep learning in the field of computer vision, convolutional neural network models and attention mechanisms have been widely applied in SAR image target recognition. The improvement of convolutional neural network attention in existing SAR image target recognition focuses on spatial and channel information but lacks research on the relationship and recognition mechanism between spatial and channel information. In response to this issue, this article proposes a hybrid attention module and introduces a Mixed Attention (MA) mechanism module in the MobileNetV2 network. The

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Li, Yan, and Juanyan Fang. "Detection of Surface Defects of Magnetic Tiles Based on Improved YOLOv5." Journal of Sensors 2023 (June 22, 2023): 1–12. http://dx.doi.org/10.1155/2023/2466107.

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The typical defect detection algorithm is ineffective due to the contrast between the magnetic tile defect and the various defect features. An improved YOLOv5-based algorithm, for detecting magnetic tile defects with varying defect features, is suggested. The procedure begins by incorporating the CBAM into feature extraction network of YOLOv5. It improves the feature of network learning capabilities for the target region by filtering and weighting the feature vectors in such a way that the processing of network is dominated by the essential target characteristics. A new loss function of detect

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Lin, Zhongwu, Min Huang, and Qinghui Zhou. "Infrared small target detection based on YOLO v4." Journal of Physics: Conference Series 2450, no. 1 (2023): 012019. http://dx.doi.org/10.1088/1742-6596/2450/1/012019.

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Abstract Infrared imaging is highly stealthy, reconnaissance and resistant to interference and currently has numerous practical applications in many fields, particularly in the military and civilian sectors. Infrared images are prone to drowning small targets in the background due to the lack of textural detail and the presence of a large amount of background noise. Small target detection on infrared images is therefore very challenging, and traditional model-driven algorithms are no longer very applicable in the face of complex and variable infrared images, so more and more people are turning

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Dong, Rongsheng, Ming Liu, and Fengying Li. "Multilayer Convolutional Feature Aggregation Algorithm for Image Retrieval." Mathematical Problems in Engineering 2019 (June 26, 2019): 1–12. http://dx.doi.org/10.1155/2019/9794202.

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In image retrieval tasks, the single-layer convolutional feature has insufficient image semantic representation ability. A new image description algorithm ML-RCroW based on multilayer multiregion cross-weighted aggregational deep convolutional features is proposed. First, the ML-RCroW algorithm inputs an image into the VGG16 (a deep convolutional neural network developed by researchers at Visual Geometry Group and Google DeepMind) network model in which the fully connected layer is discarded. The visual feature information in the convolutional neural network (CNN) is extracted, and the target

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Hu, Yufeng, Shaoping Xu, Xiaohui Cheng, Changfei Zhou, and Minghai Xiong. "AFSFusion: An Adjacent Feature Shuffle Combination Network for Infrared and Visible Image Fusion." Applied Sciences 13, no. 9 (2023): 5640. http://dx.doi.org/10.3390/app13095640.

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To obtain fused images with excellent contrast, distinct target edges, and well-preserved details, we propose an adaptive image fusion network called the adjacent feature shuffle-fusion network (AFSFusion). The proposed network adopts a UNet-like architecture and incorporates key refinements to enhance network architecture and loss functions. Regarding the network architecture, the proposed two-branch adjacent feature fusion module, called AFSF, expands the number of channels to fuse the feature channels of several adjacent convolutional layers in the first half of the AFSFusion, enhancing its

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Mohammed Almansor, Mohammed Abbas, Chongfu Zhang, Wasiq Khan, Abir Hussain, and Naji Alhusaini. "Cross Lingual Sentiment Analysis: A Clustering-Based Bee Colony Instance Selection and Target-Based Feature Weighting Approach." Sensors 20, no. 18 (2020): 5276. http://dx.doi.org/10.3390/s20185276.

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The lack of sentiment resources in poor resource languages poses challenges for the sentiment analysis in which machine learning is involved. Cross-lingual and semi-supervised learning approaches have been deployed to represent the most common ways that can overcome this issue. However, performance of the existing methods degrades due to the poor quality of translated resources, data sparseness and more specifically, language divergence. An integrated learning model that uses a semi-supervised and an ensembled model while utilizing the available sentiment resources to tackle language divergenc

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Vora, Dhvani Sandip, Shashank Yadav, and Durai Sundar. "Hybrid Multitask Learning Reveals Sequence Features Driving Specificity in the CRISPR/Cas9 System." Biomolecules 13, no. 4 (2023): 641. http://dx.doi.org/10.3390/biom13040641.

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CRISPR/Cas9 technology is capable of precisely editing genomes and is at the heart of various scientific and medical advances in recent times. The advances in biomedical research are hindered because of the inadvertent burden on the genome when genome editors are employed—the off-target effects. Although experimental screens to detect off-targets have allowed understanding the activity of Cas9, that knowledge remains incomplete as the rules do not extrapolate well to new target sequences. Off-target prediction tools developed recently have increasingly relied on machine learning and deep learn

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Gao, Yuefang, Yiteng Cai, Xuanming Bi, Bizheng Li, Shunpeng Li, and Weiping Zheng. "Cross-Domain Facial Expression Recognition through Reliable Global–Local Representation Learning and Dynamic Label Weighting." Electronics 12, no. 21 (2023): 4553. http://dx.doi.org/10.3390/electronics12214553.

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Cross-Domain Facial Expression Recognition (CD-FER) aims to develop a facial expression recognition model that can be trained in one domain and deliver consistent performance in another. CD-FER poses a significant challenges due to changes in marginal and class distributions between source and target domains. Existing methods primarily emphasize achieving domain-invariant features through global feature adaptation, often neglecting the potential benefits of transferable local features across different domains. To address this issue, we propose a novel framework for CD-FER that combines reliabl

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Zhang, Yu, and Haiqing Liu. "Traffic Participant Classification Method Based on Decision Tree for Point Cloud Data Detected by Event Camera." Journal of Physics: Conference Series 2491, no. 1 (2023): 012002. http://dx.doi.org/10.1088/1742-6596/2491/1/012002.

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Abstract Event camera has the advantage of accurately identifying moving targets while being insensitive to stationary targets, which makes up for the lack of traditional video streaming camera and has a wide range of applications in the field of traffic flow detection. In this paper, a traffic participant classification method based on a decision tree for point cloud data acquired by an event camera in a roadside installation scenario is proposed. For traffic identification, 5 basic features to describe the geometrical and quantitative characteristics, and 8 Gaussian projection features to de

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Hu, Wenting, Yin Sheng, and Xianjun Zhu. "A Semantic Image Retrieval Method Based on Interest Selection." Wireless Communications and Mobile Computing 2022 (February 27, 2022): 1–6. http://dx.doi.org/10.1155/2022/3029866.

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There is a semantic gap between people’s understanding of images and the underlying visual features of images, which makes it difficult for image retrieval results to meet the needs of individual interests. To overcome the semantic gap in image retrieval, this paper proposes a semantic image retrieval method based on interest selection. This method analyses the interest points of individual selections and gives the weight of the interest selection in different regions of an image. By extracting the underlying visual features of different regions, this paper constructs two feature vector method

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Deng, Wen, Qiang Xu, Si Li, Xiang Wang, and Zhitao Huang. "Cross-Domain Automatic Modulation Classification Using Multimodal Information and Transfer Learning." Remote Sensing 15, no. 15 (2023): 3886. http://dx.doi.org/10.3390/rs15153886.

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Automatic modulation classification (AMC) based on deep learning (DL) is gaining increasing attention in dynamic spectrum access for 5G/6G wireless communications. However, inconsistent feature parameters between the training (source) and testing (target) data lead to performance degradation or even failure of existing DL-based AMC. The primary reason for this is the difficulty in obtaining sufficient labeled training data in the target domain. Therefore, we propose a novel cross-domain AMC algorithm based on multimodal information and transfer learning, utilizing abundant unlabeled target dom

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Gong, Jie, Yang Cao, Miao Zijing, and Qiaosen Chen. "MFEE: a multi-word lexical feature enhancement framework for Chinese geological hazard event extraction." PeerJ Computer Science 9 (March 13, 2023): e1275. http://dx.doi.org/10.7717/peerj-cs.1275.

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Event Extraction (EE) is an essential and challenging task in information extraction. Most existing event extraction methods do not specifically target the Chinese geological hazards domain. This is due to the unique characteristics of the Chinese language and the lack of Chinese geological hazard datasets. To address these challenges, we propose a novel multi-word lexical feature enhancement framework (MFEE). It effectively implements Chinese event extraction in the geological hazard domain by introducing lexical information and the designed lexical feature weighting decision method. In addit

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Ye, Ziran, Yongyong Fu, Muye Gan, Jinsong Deng, Alexis Comber, and Ke Wang. "Building Extraction from Very High Resolution Aerial Imagery Using Joint Attention Deep Neural Network." Remote Sensing 11, no. 24 (2019): 2970. http://dx.doi.org/10.3390/rs11242970.

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Automated methods to extract buildings from very high resolution (VHR) remote sensing data have many applications in a wide range of fields. Many convolutional neural network (CNN) based methods have been proposed and have achieved significant advances in the building extraction task. In order to refine predictions, a lot of recent approaches fuse features from earlier layers of CNNs to introduce abundant spatial information, which is known as skip connection. However, this strategy of reusing earlier features directly without processing could reduce the performance of the network. To address

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Dai, Yong, Jian Liu, Xiancong Ren, and Zenglin Xu. "Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7618–25. http://dx.doi.org/10.1609/aaai.v34i05.6262.

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Multi-source unsupervised domain adaptation (MS-UDA) for sentiment analysis (SA) aims to leverage useful information in multiple source domains to help do SA in an unlabeled target domain that has no supervised information. Existing algorithms of MS-UDA either only exploit the shared features, i.e., the domain-invariant information, or based on some weak assumption in NLP, e.g., smoothness assumption. To avoid these problems, we propose two transfer learning frameworks based on the multi-source domain adaptation methodology for SA by combining the source hypotheses to derive a good target hypo

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Yu, Meng, Shaojie Han, Tengfei Wang, and Haiyan Wang. "An Approach to Accurate Ship Image Recognition in a Complex Maritime Transportation Environment." Journal of Marine Science and Engineering 10, no. 12 (2022): 1903. http://dx.doi.org/10.3390/jmse10121903.

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In order to monitor traffic in congested waters, permanent video stations are now commonly used on interior riverbank bases. It is frequently challenging to identify ships properly and effectively in such images because of the intricate backdrop scenery and overlap between ships brought on by the fixed camera location. This work proposes Ship R-CNN(SR-CNN), a Faster R-CNN-based ship target identification algorithm with improved feature fusion and non-maximum suppression (NMS). The SR-CNN approach can produce more accurate target prediction frames for prediction frames with distance intersectio

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Li, Zongyi, Yuxuan Shi, Hefei Ling, Jiazhong Chen, Qian Wang, and Fengfan Zhou. "Reliability Exploration with Self-Ensemble Learning for Domain Adaptive Person Re-identification." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1527–35. http://dx.doi.org/10.1609/aaai.v36i2.20043.

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Person re-identifcation (Re-ID) based on unsupervised domain adaptation (UDA) aims to transfer the pre-trained model from one labeled source domain to an unlabeled target domain. Existing methods tackle this problem by using clustering methods to generate pseudo labels. However, pseudo labels produced by these techniques may be unstable and noisy, substantially deteriorating models’ performance. In this paper, we propose a Reliability Exploration with Self-ensemble Learning (RESL) framework for domain adaptive person ReID. First, to increase the feature diversity, multiple branches are present

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Tang, Lindong, Lijun Yun, Zaiqing Chen, and Feiyan Cheng. "HRYNet: A Highly Robust YOLO Network for Complex Road Traffic Object Detection." Sensors 24, no. 2 (2024): 642. http://dx.doi.org/10.3390/s24020642.

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Object detection is a crucial component of the perception system in autonomous driving. However, the road scene presents a highly intricate environment where the visibility and characteristics of traffic targets are susceptible to attenuation and loss due to various complex road scenarios such as lighting conditions, weather conditions, time of day, background elements, and traffic density. Nevertheless, the current object detection network must exhibit more learning capabilities when detecting such targets. This also exacerbates the loss of features during the feature extraction and fusion pr

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Ren, Doudou, Wenzhong Yang, Zhifeng Lu, Danny Chen, and Houwang Shi. "Improved Weed Detection in Cotton Fields Using Enhanced YOLOv8s with Modified Feature Extraction Modules." Symmetry 16, no. 4 (2024): 450. http://dx.doi.org/10.3390/sym16040450.

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Weed detection plays a crucial role in enhancing cotton agricultural productivity. However, the detection process is subject to challenges such as target scale diversity and loss of leaf symmetry due to leaf shading. Hence, this research presents an enhanced model, EY8-MFEM, for detecting weeds in cotton fields. Firstly, the ALGA module is proposed, which combines the local and global information of feature maps through weighting operations to better focus on the spatial information of feature maps. Following this, the C2F-ALGA module was developed to augment the feature extraction capability

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Chang, Jing, Xiaohui He, Panle Li, et al. "Multi-Scale Attention Network for Building Extraction from High-Resolution Remote Sensing Images." Sensors 24, no. 3 (2024): 1010. http://dx.doi.org/10.3390/s24031010.

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The precise building extraction from high-resolution remote sensing images holds significant application for urban planning, resource management, and environmental conservation. In recent years, deep neural networks (DNNs) have garnered substantial attention for their adeptness in learning and extracting features, becoming integral to building extraction methodologies and yielding noteworthy performance outcomes. Nonetheless, prevailing DNN-based models for building extraction often overlook spatial information during the feature extraction phase. Additionally, many existing models employ a si

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Jiang, Weina, Lin Shi, Qun Niu, and Ning Liu. "Fast Radio Map Construction with Domain Disentangled Learning for Wireless Localization." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 3 (2023): 1–27. http://dx.doi.org/10.1145/3610922.

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The accuracy of wireless fingerprint-based indoor localization largely depends on the precision and density of radio maps. Although many research efforts have been devoted to incremental updating of radio maps, few consider the laborious initial construction of a new site. In this work, we propose an accurate and generalizable framework for efficient radio map construction, which takes advantage of readily-available fine-grained radio maps and constructs fine-grained radio maps of a new site with a small proportion of measurements in it. Specifically, we regard radio maps as domains and propos

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Zhang, Shuili, Kexin Zheng, and Sun Huaiyuan. "Analysis of the Occlusion Interference Problem in Target Tracking." Mathematical Problems in Engineering 2022 (September 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/4605111.

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As an indispensable part in the field of computer vision, target tracking has been widely used in intelligent transportation, missile guidance, unmanned aerial vehicle (UAV) tracking, and many other fields. It has become one of the hot directions in computer vision in recent years, while occlusion problem has always been a great difficulty and challenge in the process of target tracking. In this article, the problem of occlusion interference in target tracking is described, and the solution of occlusion problem is proposed based on different occlusion conditions. Due to the disadvantages of fe

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Rius, Jordi. "XLENS, a direct methods program based on the modulus sum function: Its application to powder data." Powder Diffraction 14, no. 4 (1999): 267–73. http://dx.doi.org/10.1017/s0885715600010654.

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XLENS is a traditional direct methods program working exclusively in reciprocal space. The distinctive feature of XLENS is the use of the modulus sum function as target function for the phase refinement. Due to its efficiency, robustness, and no need of weighting schemes, this function is specially well suited for treating powder diffraction data. The mathematical basis as well as the significance of the most important control parameters of the program will be described here. To illustrate how XLENS works, three different examples will be shown. Due to its simplicity, the modulus sum function

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Yang, Jian, Chen Li, and Xuelong Li. "AA-LMM: Robust Accuracy-Aware Linear Mixture Model for Remote Sensing Image Registration." Remote Sensing 15, no. 22 (2023): 5314. http://dx.doi.org/10.3390/rs15225314.

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Remote sensing image registration has been widely applied in military and civilian fields, such as target recognition, visual navigation and change detection. The dynamic changes in the sensing environment and sensors bring differences to feature point detection in amount and quality, which is still a common and intractable challenge for feature-based registration approaches. With such multiple perturbations, the extracted feature points representing the same physical location in space may have different location accuracy. Most existing matching methods focus on recovering the optimal feature

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Liu, Yan, Bin Guo, Daqing Zhang, et al. "Knowledge Transfer with Weighted Adversarial Network for Cold-Start Store Site Recommendation." ACM Transactions on Knowledge Discovery from Data 15, no. 3 (2021): 1–27. http://dx.doi.org/10.1145/3442203.

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Store site recommendation aims to predict the value of the store at candidate locations and then recommend the optimal location to the company for placing a new brick-and-mortar store. Most existing studies focus on learning machine learning or deep learning models based on large-scale training data of existing chain stores in the same city. However, the expansion of chain enterprises in new cities suffers from data scarcity issues, and these models do not work in the new city where no chain store has been placed (i.e., cold-start problem). In this article, we propose a unified approach for co

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Bingham, Geoffrey P., and Mark A. Mon-Williams. "The dynamics of sensorimotor calibration in reaching-to-grasp movements." Journal of Neurophysiology 110, no. 12 (2013): 2857–62. http://dx.doi.org/10.1152/jn.00112.2013.

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Reach-to-grasp movements require information about the distance and size of target objects. Calibration of this information could be achieved via feedback information (visual and/or haptic) regarding terminal accuracy when target objects are grasped. A number of reports suggest that the nervous system alters reach-to-grasp behavior following either a visual or haptic error signal indicating inaccurate reaching. Nevertheless, the reported modification is generally partial (reaching is changed less than predicted by the feedback error), a finding that has been ascribed to slow adaptation rates.

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Schebek, Liselotte, Andrea Gassmann, Elisabeth Nunweiler, Steffen Wellge, and Moritz Werthen. "Eco-Factors for International Company Environmental Management Systems." Sustainability 13, no. 24 (2021): 13897. http://dx.doi.org/10.3390/su132413897.

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Environmental management systems (EMS) require the assessment of environmental aspects to ensure that organizations recognize their most relevant impacts on the environment. The ecological scarcity method (ESM) provides weighting factors for environmental flows (pollutants and resources), called eco-factors (EF), applicable in the assessment of environmental aspects. EF are based on a distance-to-target approach, displaying the ratio of the current state to the respective policy targets for environmental flows. The ESM has been developed for Switzerland; however, for site-specific application

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Yan, Li, Ziqi Wang, Yi Liu, and Zhiyun Ye. "Generic and Automatic Markov Random Field-Based Registration for Multimodal Remote Sensing Image Using Grayscale and Gradient Information." Remote Sensing 10, no. 8 (2018): 1228. http://dx.doi.org/10.3390/rs10081228.

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The automatic image registration serves as a technical prerequisite for multimodal remote sensing image fusion. Meanwhile, it is also the technical basis for change detection, image stitching and target recognition. The demands of subpixel level registration accuracy can be rarely satisfied with a multimodal image registration method based on feature matching. In light of this, we propose a Generic and automatic Markov Random Field (MRF)-based registration framework of multimodal image using grayscale and gradient information. The proposed approach performs non-rigid registration and formulate

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Liu, Yu-Yu, Ling-Xia Mu, Peng-Ju Zhang, and Ding Liu. "Research on Classification Algorithm of Silicon Single-Crystal Growth Temperature Gradient Trend Based on Multi-Level Feature Fusion." Sensors 24, no. 4 (2024): 1254. http://dx.doi.org/10.3390/s24041254.

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In the process of silicon single-crystal preparation, the timely identification and adjustment of abnormal conditions are crucial. Failure to promptly detect and resolve issues may result in a substandard silicon crystal product quality or even crystal pulling failure. Therefore, the early identification of abnormal furnace conditions is essential for ensuring the preparation of perfect silicon single crystals. Additionally, since the thermal field is the fundamental driving force for stable crystal growth and the primary assurance of crystal quality, this paper proposes a silicon single-cryst

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Ma, Fuqiang, Jie He, and Xiaotong Zhang. "Median-Difference Correntropy for DOA under the Impulsive Noise Environment." Wireless Communications and Mobile Computing 2019 (October 3, 2019): 1–12. http://dx.doi.org/10.1155/2019/8107176.

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The source localization using direction of arrival (DOA) of target is an important research in the field of Internet of Things (IoTs). However, correntropy suffers the performance degradation for direction of arrival when the two signals contain the similar impulsive noise, which cannot be detected by the difference between two signals. This paper proposes a new correntropy, called the median-difference correntropy, which combines the generalized correntropy and the median difference. The median difference is defined as the deviation between the sampling value and the median of the signal, and

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Wang, Guirong, Jiahao Chen, Ming Dai, and Enhui Zheng. "WAMF-FPI: A Weight-Adaptive Multi-Feature Fusion Network for UAV Localization." Remote Sensing 15, no. 4 (2023): 910. http://dx.doi.org/10.3390/rs15040910.

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UAV localization in denial environments is a hot research topic in the field of cross-view geo-localization. The previous methods tried to find the corresponding position directly in the satellite image through the UAV image, but they lacked the consideration of spatial information and multi-scale information. Based on the method of finding points with an image, we propose a novel architecture—a Weight-Adaptive Multi-Feature fusion network for UAV localization (WAMF-FPI). We treat this positioning as a low-level task and achieve more accurate localization by restoring the feature map to the re

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