SOCA-PRNet: Spatially Oriented Attention-Infused Structured-Feature-Enabled PoseResNet for 2D Human Pose Estimation by Ali Zakir, Sartaj Ahmed Salman, Hiroki Takahashi

“…Despite challenges such as occlusion, unfavorable lighting, and motion blur, advancements in deep learning have significantly enhanced the performance of 2D HPE by enabling automatic feature learning from data and improving model generalization. …”
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A Dual CNN for Image Super-Resolution by Jiagang Song, Jingyu Xiao, Chunwei Tian, Yuxuan Hu, Lei You, Shichao Zhang

“…To obtain more high-frequency features, a feature learning block is used to learn more details of high-frequency information. …”
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A facial depression recognition method based on hybrid multi-head cross attention network by Yutong Li, Zhenyu Liu, Li Zhou, Xiaoyan Yuan, Zixuan Shangguan, Xiping Hu, Bin Hu

“…The first stage consists of the Grid-Wise Attention block (GWA) and Deep Feature Fusion block (DFF) for the low-level visual depression feature learning. In the second stage, we obtain the global representation by encoding high-order interactions among local features with Multi-head Cross Attention block (MAB) and Attention Fusion block (AFB).ResultsWe experimented on AVEC2013 and AVEC2014 depression datasets. …”
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Identification of Plant Disease Based on Multi-Task Continual Learning by Yafeng Zhao, Chenglong Jiang, Dongdong Wang, Xiaolu Liu, Wenhua Song, Junfeng Hu

“…The first stage is the scalable feature learning phase, where the previous feature representation is fixed. …”
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MSNet: A novel end‐to‐end single image dehazing network with multiple inter‐scale dense skip‐connections by Qiaosi Yi, Aiwen Jiang, Xiaolin Deng, Changhong Liu

“…However, most of them are concentrated on feature learning within the same block scale in isolation, and cannot perform associated analysis well on feature characteristics of different scales. …”
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ASENN: attention-based selective embedding neural networks for road distress prediction by Babitha Philip, Zhenyu Xu, Hamad AlJassmi, Qieshi Zhang, Luqman Ali

“…The results also show that the feature learning capabilities of the ASENN model improved as the number of cells increased; however, owing to the limited combination space of feature fields, extreme depths were not preferred. …”
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SAR Target Recognition via Random Sampling Combination in Open-World Environments by Xiaojing Geng, Ganggang Dong, Ziheng Xia, Hongwei Liu

“…They are further sent into the classifier for feature learning. The original open-world environment is then transformed into a closed-world environment containing the unknown class. …”
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Network Intrusion Detection Model Based on CNN and GRU by Bo Cao, Chenghai Li, Yafei Song, Yueyi Qin, Chen Chen

“…At the same time, a Gated Recurrent Unit (GRU) is used to extract the long-distance dependent information features to achieve comprehensive and effective feature learning. Finally, a softmax function is used for classification. …”
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Place Recognition with Memorable and Stable Cues for Loop Closure of Visual SLAM Systems by Rafiqul Islam, Habibullah Habibullah

“…However, the aggregation of local features arbitrarily produces a large bag-of-words vector database, limits the capability of efficient feature learning, and aggregation and querying of candidate images. …”
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Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review through Various Learning Paradigms by Lukang Wang, Min Zhang, Xu Gao, Wenzhong Shi

“…Recently, deep learning (DL) has experienced explosive growth and, with its superior capabilities in feature learning and pattern recognition, it has introduced innovative approaches to CD. …”
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Multimodal Classification Framework Based on Hypergraph Latent Relation for End-Stage Renal Disease Associated with Mild Cognitive Impairment by Xidong Fu, Chaofan Song, Rupu Zhang, Haifeng Shi, Zhuqing Jiao

“…Latent relation adaptive similarity learning (LRAS) is introduced to multi-task feature learning to construct a multimodal feature selection method based on latent relation (LRMFS). …”
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VERI-D: A new dataset and method for multi-camera vehicle re-identification of damaged cars under varying lighting conditions by Shao Liu, Sos S. Agaian

“…Our main contributions are as follows: (i) we propose a new Re-ID architecture named global–local self-attention network, which integrates local information into the feature learning process and enhances the feature representation for V-ReID and (ii) we introduce a novel damaged vehicle Re-ID dataset called VERI-D, which is the first publicly available dataset that focuses on this challenging yet practical scenario. …”
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Multi-Feature Dynamic Fusion Cross-Domain Scene Classification Model Based on Lie Group Space by Chengjun Xu, Jingqian Shu, Guobin Zhu

“…Concretely, the model first introduces Lie group feature learning and maps the samples to the Lie group manifold space. …”
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Remote Sensing Image Scene Classification with Self-Supervised Learning Based on Partially Unlabeled Datasets by Xiliang Chen, Guobin Zhu, Mingqing Liu

“…In recent years, supervised learning, represented by deep learning, has shown good performance in remote sensing image scene classification with its powerful feature learning ability. However, this method requires large-scale and high-quality handcrafted labeled datasets, which leads to a high cost of obtaining annotated samples. …”
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IterLUNet: Deep Learning Architecture for Pixel-Wise Crack Detection in Levee Systems by Manisha Panta, Md Tamjidul Hoque, Mahdi Abdelguerfi, Maik C. Flanagin

“…We propose that the feature learning be strengthened using the decoder and bottleneck feature maps by concatenating them back to the encoder blocks. …”
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Deep Learning-Based Radiomics for Prognostic Stratification of Low-Grade Gliomas Using a Multiple-Gene Signature by Mert Karabacak, Burak B. Ozkara, Kaan Senparlak, Sotirios Bisdas

“…Deep learning models, such as convolutional neural networks (CNNs), offer well-performing models and a simplified pipeline by automatic feature learning. In our study, MRI data were retrospectively obtained from The Cancer Imaging Archive (TCIA), which contains MR images for a subset of the LGG patients in The Cancer Genome Atlas (TCGA). …”
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CA-YOLO: Model Optimization for Remote Sensing Image Object Detection by Lingyun Shen, Baihe Lang, Zhengxun Song

“…These issues include weak multi-scale feature learning capabilities and the challenging trade-off between detection accuracy and model parameter complexity. …”
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Urbanization Process: A Simulation Method of Urban Expansion Based on RF-SNSCNN-CA Model by Minghao Liu, Xiangli Liao, Chun Chen

“…In response to the insufficient spatial feature learning concerning neighborhoods in traditional machine learning-based Cellular Automata (CA) models for land use change, this study couples the Random Forest (RF) model and the Spatially Non-Stationary Convolutional Neural Network (SNSCNN) model to the CA model. …”
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A novel driver emotion recognition system based on deep ensemble classification by Khalid Zaman, Sun Zhaoyun, Babar Shah, Tariq Hussain, Sayyed Mudassar Shah, Farman Ali, Umer Sadiq Khan

“…To increase the accuracy and efficiency of face detection, a new convolutional neural network block (InceptionV3) replaces the improved Faster R-CNN feature-learning block. To evaluate the proposed face detection and driver facial expression recognition (DFER) datasets, we achieved an accuracy of 98.01%, 99.53%, 99.27%, 96.81%, and 99.90% on the JAFFE, CK+, FER-2013, AffectNet, and custom-developed datasets, respectively. …”
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Mandibular Canal Segmentation From CBCT Image Using 3D Convolutional Neural Network With scSE Attention by Gang Du, Xinyu Tian, Yixu Song

“…Finally, an weighted BCE loss function was used to prevent the mental foramen and mandibular foramen areas from participating in the back-propagation calculation, and the network is more focused on the feature learning of the mandibular canal. The experimental results show that the proposed segmentation method achieves good segmentation results, with a Dice score of 85.9% and a 95% Hausdorff distance of 0.5371mm. …”
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