Building Surface Defect Detection Based on Improved YOLOv8 by Xiaoxia Lin, Yingzhou Meng, Lin Sun, Xiaodong Yang, Chunwei Leng, Yan Li, Zhenyu Niu, Weihao Gong, Xinyue Xiao

“…Methodologically, the first step involves a normalization-based attention module (NAM). This module minimizes irrelevant features and redundant information and enhances the salient feature expression of cracks, delamination, and other defects, improving feature utilization. …”
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YOLOv11-BSD: Blueberry maturity detection under simulated nighttime conditions evaluated with causal analysis by Runqing Zhang, Wenhui Dong, Pengzhi Hou, Huiqin Li, Xiongwei Han, Qingqiang Chen, Fuzhong Li, Xiaoying Zhang

“…To address these challenges, this study proposes an improved model, YOLOv11-BSD: it enhances the C3k2 module with a Bi-directional Feature Attention Mechanism to strengthen feature representation capabilities; enhances the C2PSA module using an Squeeze-and-Excitation mechanism to heighten focus on critical channel features; optimizes the PANet feature fusion pathway to improve multi-scale feature integration; and introduces the DySample module to resolve feature adaptation issues during upsampling. …”
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Enhancing Crack Segmentation Network with Multiple Selective Fusion Mechanisms by Yang Chen, Tao Yang, Shuai Dong, Like Wang, Bida Pei, Yunlong Wang

“…Initially, a star feature enhancement module is designed to resolve the issues of insufficient local feature processing and feature redundancy during the feature extraction process. …”
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Implications for developing global health education in China: evidence from an undergraduate teamwork with role-play by Chen Chen, Taufique Joarder, Chenkai Wu, Yi Cai, Hong Chen, Junxiong Pang

“…However, their inadequate background knowledge of global health issues hindered their ability to undertake the tasks in depth. …”
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MFFCI–YOLOv8: A Lightweight Remote Sensing Object Detection Network Based on Multiscale Features Fusion and Context Information by Sheng Xu, Lin Song, Junru Yin, Qiqiang Chen, Tianming Zhan, Wei Huang

“…Last, we employ the multiscale fusion lightweight neck module for more efficient multiscale feature fusion, preventing the loss of small objects. …”
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OMSF2: optimizing multi-scale feature fusion learning for pneumoconiosis staging diagnosis through data specificity augmentation by Xueting Ren, Surong Chu, Guohua Ji, Zijuan Zhao, Juanjuan Zhao, Yan Qiang, Yangyang Wei, Yan Wang

“…However, they struggle with insufficient perception of small targets and gradient inconsistency in medical image detection tasks, hindering the full utilization of multi-scale features. To address these issues, we propose an Optimized Multi-Scale Feature Fusion learning framework, OMSF2, which includes the following components: (1) Data specificity augmentation module is introduced to capture intrinsic data representations and introduce diversity by learning morphological variations and lesion locations. (2) Multi-scale feature learning module is utilized that refines micro-feature localization guided by heatmaps, enabling full extraction of multi-directional features of subtle diffuse targets. (3) Multi-scale feature fusion module is employed that facilitates the fusion of high-level and low-level features to better understand subtle differences between disease stages. …”
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CIA-UNet: An Attention-Enhanced Multi-Scale U-Net for Single Tree Crown Segmentation by Jiapeng Duan, Chuanzhao Tian, Wansi Liu, Lixiang Cao, Teng Feng, Xiaomin Tian

“…The Convolutional Block Attention Module (CBAM) is employed to increase the weight of key features, and mitigate false segmentation caused by feature confusion. …”
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DSGAU: Dual-Scale Graph Attention U-Nets for Hyperspectral Image Classification With Limited Samples by Hongzhuang Ji, Leying Song, Zhaohui Xue, Hongjun Su

“…Second, we design a dual-scale constrained graph U-Nets encoder, and use an attention feature fusion module dynamically weights these multiscale representations using channel-wise attention coefficients, effectively resolving feature redundancy issues. …”
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S²PNet: An Interactive Learning Framework for Addressing Spatial–Spectral Heterogeneity in H² Imagery Classification by Shuai Zhang, Yonghua Jiang, Chengjun Wang, Meilin Tan, Bin Du, Feng Tian

“…First, a multistage spectral purification module is designed to purify noisy information and mitigate spectral heterogeneity, achieving interaction between spectral optimization and classification. …”
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LBT-YOLO: A Lightweight Road Targeting Algorithm Based on Task Aligned Dynamic Detection Heads by Pei Tang, Zhenyu Ding, Minnan Jiang, Weikai Xu, Mao Lv

“…Autonomous driving technology plays a key role in addressing traffic safety issues and relieving traffic congestion by virtue of its capabilities of enabling accurate environmental perception and real-time response. …”
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3L-YOLO: A Lightweight Low-Light Object Detection Algorithm by Zhenqi Han, Zhen Yue, Lizhuang Liu

“…Object detection in low-light conditions presents significant challenges due to issues such as weak contrast, high noise, and blurred boundaries. …”
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A Temporal Network Based on Characterizing and Extracting Time Series in Copper Smelting for Predicting Matte Grade by Junjia Zhang, Zhuorui Li, Enzhi Wang, Bin Yu, Jiangping Li, Jun Ma

“…Finally, we implemented the TCN-TMHA module and used specific weighting mechanisms to assign weights to the input features and prioritize relevant key time step features. …”
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EMHANet: Lightweight Salient Object Detection for Remote Sensing Images via Edge-Aware Multiscale Feature Fusion by Qian Tang, Zhen Wang, Xuqi Wang, Shan-Wen Zhang

“…EMHANet consists of MobileNetV3 for feature extraction, an Edge Feature Integration Module (EFIM) for low-level edge details, a Multi-scale Contextual Information Enhancement Module (MCIEM) for high-level feature refinement, and a lightweight decoder for saliency prediction. …”
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SMM-POD: Panoramic 3D Object Detection via Spherical Multi-Stage Multi-Modal Fusion by Jinghan Zhang, Yusheng Yang, Zhiyuan Gao, Hang Shi, Yangmin Xie

“…A cross-attention-based feature extraction module and a transformer encoder–decoder with spherical positional encoding enable the accurate and efficient fusion of image and point cloud features. …”
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Attention activation network for bearing fault diagnosis under various noise environments by Yu Zhang, Lianlei Lin, Junkai Wang, Wei Zhang, Sheng Gao, Zongwei Zhang

“…Proactive monitoring and diagnosing of bearing faults can prevent significant safety issues. Among various diagnostic methods that analyze bearing vibration signals, deep learning is notably effective. …”
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AECA-FBMamba: A Framework with Adaptive Environment Channel Alignment and Mamba Bridging Semantics and Details by Xin Chai, Wenrong Zhang, Zhaoxin Li, Ning Zhang, Xiujuan Chai

“…Additionally, we incorporate the Feature Bridging Mamba (FBMamba) module, which enables smooth receptive field reduction, effectively addressing feature alignment issues when integrating local contexts into global representations. …”
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Robust UAV Tracking via Information Synergy Fusion and Multi-Dimensional Spatial Perception by Yong Lin, Mingfeng Yin, Yingjie Zhang, Xiaoteng Guo

“…Then, a multi-dimensional spatial perception (MSP) module is constructed to compute global dependencies and spatial dependency information during the tracking phase, which aggregates target feature information from different scales. …”
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MSF-ACA: Low-Light Image Enhancement Network Based on Multi-Scale Feature Fusion and Adaptive Contrast Adjustment by Zhesheng Cheng, Yingdan Wu, Fang Tian, Zaiwen Feng, Yan Li

“…To address the issues of loss of important detailed features, insufficient contrast enhancement, and high computational complexity in existing low-light image enhancing methodologies, this paper presents a low-light image enhancement network (MSF-ACA), which uses multi-scale feature fusion and adaptive contrast adjustment. …”
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Face Forgery Detection with Long-Range Noise Features and Multilevel Frequency-Aware Clues by Yi Zhao, Xin Jin, Song Gao, Liwen Wu, Shaowen Yao, Qian Jiang

“…The widespread dissemination of high-fidelity fake faces created by face forgery techniques has caused serious trust concerns and ethical issues in modern society. Consequently, face forgery detection has emerged as a prominent topic of research to prevent technology abuse. …”
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Enhanced group relation learning via aligned attention masking for fashion product captioning by Yuhao Tang, Dong Ye, Fei Tao, Guodong Du

“…This discrepancy arises when datasets contain multiple angles all paired with a single description, resulting in situations where certain textual details may not be visible in any angle. To address such issues, we propose a novel Fashion Transformer framework, which consists of two components: (1) an intra-group confusion module and an inter-group discrepancy module with dual feature interaction branches integrated into a unified model, and (2) a fashion encoder generates masks that identify the feature tokens and text tokens where information co-occurs in both modalities, ensuring precise alignment between the fashion images and texts. …”
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