Non-end-to-end adaptive graph learning for multi-scale temporal traffic flow prediction. by Kang Xu, Bin Pan, MingXin Zhang, Xuan Zhang, XiaoYu Hou, JingXian Yu, ZhiZhu Lu, Xiao Zeng, QingQing Jia

“…The method incorporates a multi-scale temporal attention module and a multi-scale temporal convolution module to extract multi-scale information. …”
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Research on Microscale Vehicle Logo Detection Based on Real-Time DEtection TRansformer (RT-DETR) by Meiting Jin, Junxing Zhang

“…Vehicle logo detection (VLD) is a critical component of intelligent transportation systems (ITS), particularly for vehicle identification and management in dynamic traffic environments. However, traditional object detection methods are often constrained by image resolution, with vehicle logos in existing datasets typically measuring 32 × 32 pixels. …”
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A lightweight and efficient gesture recognizer for traffic police commands using spatiotemporal feature fusion by Jun Xiao, Honghan Li, Ji Zhao

“…It not only recognizes traffic police gestures in real-time with high efficiency but also demonstrates strong robustness and Credibility in recognizing gestures in complex environments and dynamic scenarios.…”
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World model-based end-to-end scene generation for accident anticipation in autonomous driving by Yanchen Guan, Haicheng Liao, Chengyue Wang, Xingcheng Liu, Jiaxun Zhang, Zhenning Li

“…In parallel, we construct a dynamic prediction model that encodes spatio-temporal relationships through strengthened graph convolutions and dilated temporal operators, effectively addressing data incompleteness and transient visual noise. …”
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An Intelligent Field Monitoring System Based on Enhanced YOLO-RMD Architecture for Real-Time Rice Pest Detection and Management by Jiangdong Yin, Jun Zhu, Gang Chen, Lihua Jiang, Huanhuan Zhan, Haidong Deng, Yongbing Long, Yubin Lan, Binfang Wu, Haitao Xu

“…Addressing critical challenges in real-time detection accuracy and environmental adaptability, the proposed system integrates three innovative components: (1) a novel Receptive Field Attention Convolution module enhancing feature extraction in complex backgrounds; (2) a Mixed Local Channel Attention module balances local and global features to improve detection precision for small targets in dense foliage; (3) an enhanced multi-scale detection architecture incorporating Dynamic Head with an additional detection head, enabling simultaneous improvement in multi-scale pest detection capability and coverage. …”
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Few-shot biomedical NER empowered by LLMs-assisted data augmentation and multi-scale feature extraction by Di Zhao, Wenxuan Mu, Xiangxing Jia, Shuang Liu, Yonghe Chu, Jiana Meng, Hongfei Lin

“…Simultaneously, we employ dynamic convolution to capture multi-scale semantic information in sentences and enhance feature representation based on PubMedBERT. …”
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SCASNet: Spatial Context-Aware Selection Network for Small Object Detection in Aerial Imagery by Zhenkuan Wang, Xue-Mei Dong, Yongli Xu

“…It leverages multiscale strip convolutions to map spatial relationships and dynamically allocates weights across channel and spatial dimensions. …”
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Outdoor Dataset for Flying a UAV at an Appropriate Altitude by Theyab Alotaibi, Kamal Jambi, Maher Khemakhem, Fathy Eassa, Farid Bourennani

“…The increasing popularity of drones for Internet of Things (IoT) applications has led to significant research interest in autonomous navigation within unknown and dynamic environments. Researchers are utilizing supervised learning techniques that rely on image datasets to train drones for autonomous navigation, which are typically used for rescue, surveillance, and medical aid delivery. …”
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Adaptive Conditional Reasoning for Remote Sensing Visual Question Answering by Yiqun Gao, Zongwen Bai, Meili Zhou, Bolin Jia, Peiqi Gao, Rui Zhu

“…Therefore, we propose the Adaptive Conditional Reasoning (ACR) network, a novel framework that dynamically tailors reasoning pathways to question semantics through type-aware feature fusion. …”
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Enhanced Edge Feature Fusion and Visual State Space for Precise Detection of Switch Tip Close Fitting by Chang Zhengtang, Lan Xiangui, Lai Yihui

“…A Dynamic Coefficient Fusion Edge Line Detection Module is also incorporated to improve the precision of switch point close contact measurement. …”
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LT-YOLO: long-term temporal enhanced YOLO for stenosis detection on invasive coronary angiography by Jiaxin Li, Xiang Tang, Xuesong Wang

“…First, a spatial-aware backbone based on a dynamic Transformer and C2f Convolution of YOLOv8 combines the local and global feature extraction to distinguish the coronary regions from the background. …”
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Information Fusion of Infrared Images and Vibration Signals for Coupling Fault Diagnosis of Rotating Machinery by Tangbo Bai, Jianwei Yang, Dechen Yao, Ying Wang

“…Then, a multichannel convolution neural network-based method is constructed to achieve data-level information fusion and improve the fault diagnosis accuracy. …”
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YOLO-BOS: An Emerging Approach for Vehicle Detection with a Novel BRSA Mechanism by Liang Zhao, Lulu Fu, Xin Jia, Beibei Cui, Xianchao Zhu, Junwei Jin

“…Second, we incorporate Omni-directional Dynamic Convolution (ODConv) into the head network, which is capable of simultaneously learning complementary attention across the four dimensions of the kernel space, therefore facilitating the capture of multifaceted features from the input data. …”
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Large-Scale nighttime illuminated road extraction in the BTH region of China using SDGSAT-1 nighttime light data by Qiyuan Xie, Hui Li, Linhai Jing, Yichen Jiang, Haifeng Ding, Zhongchang Sun

“…The proposed model uses UNet as the basic architecture and incorporates a dynamic snake convolution module to increase the sensitivity of the NTIR pixels. …”
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Anomaly detection method for cyber physical power system based on bilateral data fusion by Tianlei Zang, Shijun Wang, Chuangzhi Li, Yunfei Liu, Yujian Xiao, Zian Wang, Xueying Yu

“…Second, a transformer-based detection model is established to extract dynamic network attributes and state transition patterns in cyber-side data. …”
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DSCONV-GAN: a UAV-BASED model for Verticillium Wilt disease detection in Chinese cabbage in complex growing environments by Jun Zhang, Dongfang Zhang, Jingyan Liu, Yuhong Zhou, Xiaoshuo Cui, Xiaofei Fan

“…Based on YOLOv8, with the addition of the dynamic snake convolution (DSConv) module and the improved loss function maximum possible distance intersection-over-union (MPDIoU), we acquired enhanced complex structures and global characteristics in Chinese cabbage images under different growth conditions. …”
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Medical image segmentation network based on multi-scale cross-attention and wavelet transform by Fan Qin, Yongjie Liang, Chaofeng Yang, Yulong Cao, Jiaying Fan, Peiyuan Wang, Bizhong Wei

“…The network introduces a Multi-Scale Cross-Attention Module (MSCAM) to enhance feature selection capability and long-distance dependency modeling, employs Wavelet Transform Convolution (WTConv) for effective multi-scale feature extraction, and implements a Dynamic Upsampling module (DySample) to improve global context integration and detail reconstruction. …”
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Cross-Layer Feature Fusion and Decentration Aberration Correction of Circular Points for Automated Guided Vehicle Terminal Positioning by Guiyang Zhang, Lanyu Yang, Kunkang Cao, Zengguang Man, Jian Wu, Boning Li

“…In the remote coarse positioning stage, small number of receiver domains underwent continuous multi-layer convolution, which broadened the receptive field of a single element in the feature map of a small circular target. …”
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Research on a Target Detection Algorithm for Common Pests Based on an Improved YOLOv7-Tiny Model by He Gong, Xiaodan Ma, Ying Guo

“…Lightweight Network Design: The backbone network is optimized by integrating GhostNet and Dynamic Region-Aware Convolution (DRConv) to enhance computational efficiency. 2. …”
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Panoptic Segmentation Method Based on Feature Fusion and Edge Guidance by Lanshi Yang, Shiguo Wang, Shuhua Teng

“…PSM-FFEG introduces three key components: (1) A dynamic multi-scale feature fusion module enhancing contextual modeling via cascaded convolutions and adaptive attention. (2) An explicit edge guidance module refining boundary features with dedicated supervision. (3) A dual-path Transformer decoder optimizing cross-path feature interaction between pixels and queries. …”
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