An Interpretable Siamese Attention Res-CNN for Fingerprint Spoofing Detection by Chengsheng Yuan, Zhenyu Xu, Xinting Li, Zhili Zhou, Junhao Huang, Ping Guo

Get full text

Mi-DETR: For Mitosis Detection From Breast Histopathology Images an Improved DETR by Fatma Betul Kara Ardac, Pakize Erdogmus

“…In the decoder layer, unnecessary model parameters have been filtered out using a layer reduction strategy to improve model efficiency and reduce computational costs. …”
Get full text

RTDU: Interpretable Region-Aware Transformer-Based Deep Unfolding Network for Pan-Sharpening by Shuo Wang, Genji Yuan, Jinjiang Li

Get full text

Learning Interactions between Rydberg Atoms by Olivier Simard, Anna Dawid, Joseph Tindall, Michel Ferrero, Anirvan M. Sengupta, Antoine Georges

“…Quantum simulators have the potential to solve quantum many-body problems that are beyond the reach of classical computers, especially when they feature long-range entanglement. …”
Get full text

FPGA-oriented lightweight multi-modal free-space detection network by Feiyi Fang, Junzhu Mao, Wei Yu, Jianfeng Lu

“…The pruning is in two parts. For the feature extractors, we propose a data-dependent filter pruner according to the principle that the low-rank feature map contains less information. …”
Get full text

YOLO-MES: An Effective Lightweight Underwater Garbage Detection Scheme for Marine Ecosystems by Chengxu Huang, Wenyuan Zhang, Beitian Zheng, Jiahao Li, Bochen Xie, Ruisi Nan, Zongming Tan, Baohua Tan, Neal N. Xiong

“…This paper also proposes a streamlined Slim-neck design strategy, which effectively reduces the number of parameters in the neck network while maintaining multi-scale feature fusion accuracy. …”
Get full text

Multi-Strategy Improvement of Coal Gangue Recognition Method of YOLOv11 by Hongjing Tao, Lei Zhang, Zhipeng Sun, Xinchao Cui, Weixun Yi

“…It exhibits a slight increase in computational load, despite an almost unchanged number of parameters, and demonstrates the best overall detection performance. …”
Get full text

LWheatNet: a lightweight convolutional neural network with mixed attention mechanism for wheat seed classification by Xiaojuan Guo, Jianping Wang, Guohong Gao, Zihao Cheng, Zongjie Qiao, Ranran Zhang, Zhanpeng Ma, Xing Wang

“…Each network consists of three core layers, with each core layer is comprising one downsampling unit and multiple basic units. To minimize model parameters and computational load without sacrificing performance, each unit utilizes depthwise separable convolutions, channel shuffle, and channel split techniques.ResultsTo validate the effectiveness of the proposed model, we conducted comparative experiments with five classic network models: AlexNet, VGG16, MobileNet V2, MobileNet V3, and ShuffleNet V2. …”
Get full text

Automated Early-Stage Glaucoma Detection Using a Robust Concatenated AI Model by Wheyming Song, Ing-Chou Lai

Get full text

Infrared Small-Target Detection via Improved Density Peak Clustering and Gray-Level Contribution by Lang Wu, Fan Fan, Jun Huang, Guoqiang Xiao

Get full text

A robust deep learning framework for multiclass skin cancer classification by Burhanettin Ozdemir, Ishak Pacal

“…To overcome these obstacles, this study proposes an innovative hybrid deep learning model that combines ConvNeXtV2 blocks and separable self-attention mechanisms, tailored to enhance feature extraction and optimize classification performance. …”
Get full text

Lightweight Detection Methods for Multi-Scale Targets in Complex Scenarios by Anjun Yu, Zhichao Rao, Yonghua Xiong, Jinhua She

“…In this paper, we propose LAP-YOLO (Lightweight Aggregate Perception based on “You Only Look Once”), which integrates a Feature-Aware Aggregation (FAA) module to enhance feature representation and a Lightweight Information Diffusion (LID) detection head to improve small-object detection efficiency with minimal computational overhead. …”
Get full text

ZZ-YOLOv11: A Lightweight Vehicle Detection Model Based on Improved YOLOv11 by Zhe Zhang, Zhongyang Zhang, Gang Li, Chenxi Xia

“…Secondly, to reduce the number of parameters in the detection head and to fuse the extracted features better, a self-developed Lightweight Detail Convolutional Detection Head (LDCD) detection head is introduced. …”
Get full text

Detection of egg appreance based on Fasternet and YOLOv5 model by WEI Jingxin, CHEN Zhongju, XU Haoran

Get full text

Bayesian curriculum generation in sparse reward reinforcement learning environments by Onur Akgün, N. Kemal Üre

Get full text

Two-stage spline-approximation in linear structure routing by D. A. Karpov, V. I. Struchenkov

“…A fundamental feature of the corresponding design tasks is that the plan and longitudinal profile of the route consist of elements of a given type. …”
Get full text

Multiscale Residual Weighted Classification Network for Human Activity Recognition in Microwave Radar by Yukun Gao, Lin Cao, Zongmin Zhao, Dongfeng Wang, Chong Fu, Yanan Guo

Get full text

Space Precession Target Classification Based on Radar High-Resolution Range Profiles by Yizhe Wang, Cunqian Feng, Yongshun Zhang, Sisan He

“…Effective classification of space targets is of great significance for further micromotion parameter extraction and identification. Feature extraction is a key step during the classification process, largely influencing the final classification performance. …”
Get full text

Deep Learning-Based Detection and Identification Method for Sports Health Video Dissemination by Yajun Pang

“…An athletics action estimation network (AAEN) is promoted, which initially obtains the correlation features and depth features between human skeleton key points through partial perception units. …”
Get full text

Research on RF Intensity Temperature Sensing based on 1D-CNN by DING Meiqi, GUI Lin, WANG Ziyi, SHANG Disen, QIAN Min, LI Qiankun

“…【Conclusion】1D-CNN has significant advantages in dealing with complex nonlinear relationships and feature extraction, not only superior in computational efficiency and robustness, but also effective in dealing with noise and environmental interference. …”
Get full text