Siam Deep Feature KCF Method and Experimental Study for Pedestrian Tracking by Di Tang, Weijie Jin, Dawei Liu, Jingqi Che, Yin Yang

“…In addition, a lightweight Siamese CNN with Cross Stage Partial (CSP) provided the representations of features learned from massive face images, allowing the target similarity in data association to be guaranteed. …”
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EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust Models by Ian E. Nielsen, Ravi P. Ramachandran, Nidhal Bouaynaya, Hassan M. Fathallah-Shaykh, Ghulam Rasool

“…At the same time, numerous recent research papers in the fields of machine learning and explainable artificial intelligence have demonstrated the essential role of robustness to natural noise and adversarial attacks in determining the features learned by a model. This paper focuses on evaluating methods of attribution mapping to find whether robust neural networks are more explainable, particularly within the application of classification for medical imaging. …”
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Local Feature Extraction Network for Point Cloud Analysis by Zehao Zhou, Yichun Tai, Jianlin Chen, Zhijiang Zhang

“…In this paper, we propose local feature extraction network (LFE-Net) which focus on extracting local feature for point clouds analysis. Such geometric features learning from a relation of local points can be used in a variety of shape analysis problems such as classification, part segmentation, and point matching. …”
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Deep Learning for Sensing Matrix Prediction in Computational Microwave Imaging With Coded-Apertures by Jiaming Zhang, Rahul Sharma, Maria Garcia-Fernandez, Guillermo Alvarez-Narciandi, Muhammad Ali Babar Abbasi, Okan Yurduseven

“…To achieve this, a deep learning-based approach which is capable of generating the sensing matrix using features learned directly from the coded-aperture distribution is proposed. …”
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A Systematic Approach for Explaining Time and Frequency Features Extracted by Convolutional Neural Networks From Raw Electroencephalography Data by Charles A. Ellis, Charles A. Ellis, Robyn L. Miller, Robyn L. Miller, Vince D. Calhoun, Vince D. Calhoun, Vince D. Calhoun

“…As such, a growing group of methods have been developed that provide insight into the spectral features learned by CNNs. However, spectral power is not the only important form of information within EEG, and the capacity to understand the roles of specific multispectral waveforms identified by CNNs could be very helpful. …”
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Transformer-based autoencoder with ID constraint for unsupervised anomalous sound detection by Jian Guan, Youde Liu, Qiuqiang Kong, Feiyang Xiao, Qiaoxi Zhu, Jiantong Tian, Wenwu Wang

“…However, the AE-based methods could be limited as the feature learned from normal sounds can also fit with anomalous sounds, reducing the ability of the model in detecting anomalies from sound. …”
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An Environmental Pattern Recognition Method for Traditional Chinese Settlements Using Deep Learning by Yueping Kong, Peng Xue, Yuqian Xu, Xiaolong Li

“…In this paper, we study the use of deep learning techniques to achieve automatic recognition of environmental patterns in TCSs based on environmental features learned from remote sensing images and digital elevation models. …”
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Eye Tracking for Everyone by Kellnhofer, Petr, Bhandarkar, Suchendra, Khosla, Aditya, Kannan, Harini D., Matusik, Wojciech, Torralba, Antonio

“…Further, we demonstrate that the features learned by iTracker generalize well to other datasets, achieving state-of-the-art results.…”
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Deep clustering of protein folding simulations by Debsindhu Bhowmik, Shang Gao, Michael T. Young, Arvind Ramanathan

“…In these systems, we show that the CVAE latent features learned correspond to distinct conformational substates along the protein folding pathways. …”
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Automatic Feature Selection for Improved Interpretability on Whole Slide Imaging by Antoine Pirovano, Hippolyte Heuberger, Sylvain Berlemont, SaÏd Ladjal, Isabelle Bloch

“…After training two WSI classification architectures on Camelyon-16 WSI dataset, highlighting discriminative features learned, and validating our approach with pathologists, we propose a novel manner of computing interpretability slide-level heat-maps, based on the extracted features, that improves tile-level classification performances. …”
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A Two-Branch CNN Fusing Temporal and Frequency Features for Motor Imagery EEG Decoding by Jun Yang, Siheng Gao, Tao Shen

“…TBTF-CNN fuses the features learned from the two branches and then inputs them into the classifier to decode the MI-EEG. …”
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Aspect-level sentiment classification with fused local and global context by Ao Feng, Jiazhi Cai, Zhengjie Gao, Xiaojie Li

“…To address these issues, we present the PConvBERT (Prompt-ConvBERT) and PConvRoBERTa (Prompt-ConvRoBERTa) models, in which local context features learned by a Local Semantic Feature Extractor (LSFE) are fused with the BERT/RoBERTa global features. …”
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Network-based prediction approach for cancer-specific driver missense mutations using a graph neural network by Narumi Hatano, Mayumi Kamada, Ryosuke Kojima, Yasushi Okuno

“…The prediction part learns whether variants are driver variants using features of individual variants combined with the graph features learned in the graph part. Results Net-DMPred, which considers molecular networks, performed better than conventional methods. …”
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Knowledge Graph Double Interaction Graph Neural Network for Recommendation Algorithm by Shuang Kang, Lin Shi, Zhenyou Zhang

“…Finally, the label propagation algorithm is used to train the edge weights to assist entity features learning. Experiments on two real datasets commonly used in recommended algorithms were conducted and showed that the model is better than the existing baseline models. …”
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Hybrid Siamese Network for Unconstrained Face Verification and Clustering under Limited Resources by Nehal K. Ahmed, Elsayed E. Hemayed, Magda B. Fayek

“…It reaches 99.1% on the Arabian faces dataset. Moreover, features learned by the proposed architecture are used in building a face clustering system that is based on an updated version of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). …”
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Concatenated Modified LeNet Approach for Classifying Pneumonia Images by Dhayanithi Jaganathan, Sathiyabhama Balsubramaniam, Vidhushavarshini Sureshkumar, Seshathiri Dhanasekaran

“…This enhancement aims to boost the discriminative capacity of the features learned by the model. Furthermore, we integrate batch normalization to stabilize the training process and enhance performance within smaller, less complex, CNN architectures like LeNet. …”
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Floral Categorization by Bumblebees: The Perceptual and the Functional by Vicki Xu, Catherine M. S. Plowright

“…Bees grouped flowers based on the relevant features learned through experience rather than just relying on perceptual similarity, but also do not immediately discount similar flowers during foraging, giving a preliminary insight into the role of function in perceptual similarity judgments.…”
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A Scale Sequence Object-based Convolutional Neural Network (SS-OCNN) for crop classification from fine spatial resolution remotely sensed imagery by Huapeng Li, Ce Zhang, Yong Zhang, Shuqing Zhang, Xiaohui Ding, Peter M. Atkinson

“…This scale sequence can fuse effectively the features learned at different scales by transforming progressively the information extracted at small scales to larger scales. …”
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Real-Time Fault Diagnosis for Hydraulic System Based on Multi-Sensor Convolutional Neural Network by Haohan Tao, Peng Jia, Xiangyu Wang, Liquan Wang

“…Both the sensor selection process and fault diagnosis process are based on abstract fault-related features learned by a CNN deep learning model. Therefore, compared with the traditional sensor-and-feature selection method, the proposed MS-CNN can find the sensor channels containing higher-level fault-related features, which provides two advantages for diagnosis. …”
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Feature fusion-based collaborative learning for knowledge distillation by Yiting Li, Liyuan Sun, Jianping Gou, Lan Du, Weihua Ou

“…We concatenate the features learned by the teacher and the student networks to obtain a more representative feature map for knowledge transfer. …”
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