A Location–Time-Aware Factorization Machine Based on Fuzzy Set Theory for Game Perception by Xiaoxia Xie, Zhenhong Jia, Hongzhan Shi, Xianxing Zhu

“…Then, LTFM utilizes fuzzy set theory to strengthen the positive feature interactions and reduce the negative feature interactions. …”
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Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data by Ling Dai, Guangyun Zhang, Jinqi Gong, Rongting Zhang

“…This framework is more flexible and creative than the traditional method based on laboratory research to obtain the key feature and feature interaction index for hyperspectral remotely sensed data.…”
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Community-engaged training in informed consent by Kris M. Markman, Noelle P. Weicker, Andreas K. Klein, Robert Sege

“…Based on these findings, we suggest that training on informed consent include more simulated consent exercises that feature interaction with community members who can provide real-time feedback to coordinators.…”
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HATF: Multi-Modal Feature Learning for Infrared and Visible Image Fusion via Hybrid Attention Transformer by Xiangzeng Liu, Ziyao Wang, Haojie Gao, Xiang Li, Lei Wang, Qiguang Miao

“…Current CNN-based methods for infrared and visible image fusion are limited by the low discrimination of extracted structural features, the adoption of uniform loss functions, and the lack of inter-modal feature interaction, which make it difficult to obtain optimal fusion results. …”
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Real-Time Defect Detection Model in Industrial Environment Based on Lightweight Deep Learning Network by Jiaqi Lu, Soo-Hong Lee

“…The network includes a backbone network that integrates attention layers for feature extraction, a multiscale feature aggregation network for semantic information, a residual enhancement network for spatial focus, and an attention enhancement network for global–local feature interaction. These components enhance detection performance for diverse defects while maintaining low hardware requirements. …”
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Coordinate Attention Filtering Depth-Feature Guide Cross-Modal Fusion RGB-Depth Salient Object Detection by Lingbing Meng, Mengya Yuan, Xuehan Shi, Qingqing Liu, Le Zhange, Jinhua Wu, Ping Dai, Fei Cheng

“…Many methods use the same feature interaction module to fuse RGB and depth maps, which ignores the inherent properties of different modalities. …”
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Multiscale Adaptive Fusion Network for Hyperspectral Image Denoising by Haodong Pan, Feng Gao, Junyu Dong, Qian Du

“…However, existing methods still have limitations in feature interaction exploitation among multiple scales and rich spectral structure preservation. …”
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Vison transformer adapter-based hyperbolic embeddings for multi-lesion segmentation in diabetic retinopathy by Zijian Wang, Haimei Lu, Haixin Yan, Hongxing Kan, Li Jin

“…The proposed model is primarily built on a traditional Vision Transformer encoder and further enhanced by incorporating a spatial prior module for image convolution and feature continuity, followed by feature interaction processing using the spatial feature injector and extractor. …”
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Swin Routiformer: Moss Classification Algorithm Based on Swin Transformer With Bi-Level Routing Attention by Peichen Li, Huiqin Wang, Zhan Wang, Ke Wang, Chong Wang

“…Adopting the Swin Transformer model with its multi-level hierarchical architecture for visual feature extraction, we introduce the Swin Routiformer Block, which enhances the network’s feature interaction capabilities, reduces computational complexity, and improves classification accuracy and image processing speed for moss species. …”
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Cascaded MPN: Cascaded Moment Proposal Network for Video Corpus Moment Retrieval by Sunjae Yoon, Dahyun Kim, Junyeong Kim, Chang D. Yoo

“…To overcome the aforementioned challenges, our proposed Cascaded Moment Proposal Network incorporates the following two main properties: (1) Hierarchical Semantic Reasoning which provides video understanding from anchor-free level to anchor-based level via building hierarchical video graph, and (2) Cascaded Moment Proposal Generation which precisely performs moment retrieval via devising cascaded multi-modal feature interaction among anchor-free and anchor-based video semantics. …”
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RGB depth salient object detection via cross‐modal attention and boundary feature guidance by Lingbing Meng, Mengya Yuan, Xuehan Shi, Le Zhang, Qingqing Liu, Dai Ping, Jinhua Wu, Fei Cheng

“…A novel end‐to‐end framework is proposed for RGB‐D SOD, which comprises of four main components: the cross‐modal attention feature enhancement (CMAFE) module, the multi‐level contextual feature interaction (MLCFI) module, the boundary feature extraction (BFE) module, and the multi‐level boundary attention guidance (MLBAG) module. …”
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Research on a Knowledge Graph Embedding Method Based on Improved Convolutional Neural Networks for Hydraulic Engineering by Yang Liu, Jiayun Tian, Xuemei Liu, Tianran Tao, Zehong Ren, Xingzhi Wang, Yize Wang

“…In response to the shortcomings of existing knowledge graph embedding strategies, such as weak feature interaction and latent knowledge representation, a unique hydraulic knowledge graph embedding method is suggested. …”
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HyperLiteNet: Extremely Lightweight Non-Deep Parallel Network for Hyperspectral Image Classification by Jianing Wang, Runhu Huang, Siying Guo, Linhao Li, Zhao Pei, Bo Liu

“…Meanwhile, an elaborately designed feature-interaction module is constructed to acquire and fuse generalized abstract spectral and spatial features in different parallel layers. …”
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A Knowledge-Graph-Driven Method for Intelligent Decision Making on Power Communication Equipment Faults by Huiying Qu, Yiying Zhang, Kun Liang, Siwei Li, Xianxu Huo

“…The user intent multi-feature learning recommendation model uses a graph convolutional neural network to aggregate the higher-order neighborhood information of faulty entities and then the cross-compression matrix to solve the feature interaction degree of the user and graph, which achieves accurate prediction of fault retrieval. …”
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Multi-Resolution and Semantic-Aware Bidirectional Adapter for Multi-Scale Object Detection by Zekun Li, Jin Pan, Peidong He, Ziqi Zhang, Chunlu Zhao, Bing Li

“…In order to fully leverage the features of multi-scale objects and amplify feature interaction and representation, we introduce a novel and efficient framework known as a multi-resolution and semantic-aware bidirectional adapter (MSBA). …”
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A Hybrid Feature Selection and Multi-Label Driven Intelligent Fault Diagnosis Method for Gearbox by Di Liu, Xiangfeng Zhang, Zhiyu Zhang, Hong Jiang

“…The method takes into account the consideration of feature irrelevance, redundancy and inter-feature interaction in the selection process, and the selected optimal subsets have better diagnostic performance. …”
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Contrastive learning-based knowledge distillation for RGB-thermal urban scene semantic segmentation by Guo, Xiaodong, Zhou, Wujie, Liu, Tong

“…They included a multi-attribute hierarchical feature interaction module (MHFI) and a detail-guided semantic decoder (DGSD). …”
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A Region-Based Feature Fusion Network for VHR Image Change Detection by Pan Chen, Cong Li, Bing Zhang, Zhengchao Chen, Xuan Yang, Kaixuan Lu, Lina Zhuang

“…RFNet uses a fully convolutional Siamese network backbone where a multi-stage feature interaction module (MFIM) is embedded in the dual encoder and a series of region-based feature fusion modules (RFFMs) is used to generate change information. …”
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Augmented Transformer network for MRI brain tumor segmentation by Muqing Zhang, Dongwei Liu, Qiule Sun, Yutong Han, Bin Liu, Jianxin Zhang, Mingli Zhang

“…These augmented modules are strategically placed at the bottleneck of the segmentation network, forming multi-head self-attention blocks and circulant projections, aiming to maintain feature diversity and enhance feature interaction and diversity. Furthermore, paired attention modules operate from low to high layers throughout the network, establishing long-range relationships in both spatial and channel dimensions. …”
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Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network by Maryam Doborjeh, Zohreh Doborjeh, Nikola Kasabov, Molood Barati, Grace Y. Wang

“…The research contributes to: (1) enhanced interpretability of SNN learning behavior through dynamic neural clustering; (2) feature selection and enhanced accuracy of classification; (3) spatiotemporal rules to support model explainability; and (4) a better understanding of the dynamics in STBD in terms of feature interaction. The clustering method was applied to a case study of Electroencephalogram (EEG) data, recorded from a healthy control group (<i>n</i> = 21) and opiate use (<i>n</i> = 18) subjects while they were performing a cognitive task. …”
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