As mobile computing technology rapidly evolves, deploying efficient object detection algorithms on mobile devices emerges as a pivotal research area in computer vision. This study zeroes in on optimizing the YOLOv7 algorithm to boost its operational efficiency and speed on mobile platforms while ensuring high accuracy. Leveraging a synergy of advanced techniques such as Group Convolution, ShuffleNetV2, and Vision Transformer, this research has effectively minimized the model's parameter count and memory usage, streamlined the network architecture, and fortified the real-time object detection proficiency on resource-constrained devices. The experimental outcomes reveal that the refined YOLO model demonstrates exceptional performance, markedly enhancing processing velocity while sustaining superior detection accuracy.
Rethinking Local Perception in Lightweight Vision Transformer
Tsinghua University（清华大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Lightweight Real-time Semantic Segmentation Network with Efficient Transformer and CNN
Nanjing University of Posts and Telecommunications（南京邮电大学）；Soochow University（苏州大学）；Shanghai University（上海大学）；Nanjing University of Science and Technology（南京理工大学）；Kyushu Institute of Technology（九州工业大学）
Shaanxi University of Science and Technology（陕西科技大学）；Xi’an University of Posts and Telecommunications（西安邮电大学）；Xi’an University of Technology（西安理工大学）；Bielefeld University（比勒费尔德大学）；Brunel University London（伦敦布鲁内尔大学）；Xi’an Jiaotong University（西安交通大学）
HAFormer: Unleashing the Power of Hierarchy-Aware Features for Lightweight Semantic Segmentation
University of Technology Sydney（悉尼科技大学）；Nanjing University（南京大学）；Honor Device Company, Ltd（荣耀设备有限公司）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Key Laboratory of Artificial Intelligence, Ministry of Education（教育部人工智能重点实验室）
Efficient Semantic Segmentation via Lightweight Multiple-Information Interaction Network
Institute of Advanced Technology, Nanjing University of Posts and Telecommunications（南京邮电大学先进技术研究院）；Provincial Key Laboratory for Computer Information Processing Technology, Soochow University（苏州大学计算机信息处理技术省重点实验室）；Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼科技大学工程与信息技术学院）；Tianyijiaotong Technology Ltd.（天宜交通科技有限公司）；Graduate School of Advanced Science and Engineering, Hiroshima University（广岛大学先进科学与工程研究院）；Department of Electrical Engineering, National Tsing Hua University（国立清华大学电机工程学系）
HDiffTG: A Lightweight Hybrid Diffusion-Transformer-GCN Architecture for 3D Human Pose Estimation
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；Faculty of Science and Technology, University of Macau（澳门大学科学与技术学院）；College of Mechatronics and Control Engineering, Shenzhen University（深圳大学机电与控制工程学院）；School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学人工智能与高级计算学院）；School of Computing and Information Systems, Singapore Management University（新加坡管理学院计算机与信息系统学院）
Harnessing Lightweight Transformer with Contextual Synergic Enhancement for Efficient 3D Medical Image Segmentation
Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR（电子工程系，香港中文大学（深圳）Hong Kong SAR）；Centre for Artificial Intelligence and Robotics (CAIR), Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong SAR（人工智能与机器人中心（CAIR），香港科学与创新研究院，中国科学院，Hong Kong SAR）
LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation
School of Computer Science, Peking University（北京大学计算机学院）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Key Laboratory of High Confidence Software Technologies, Ministry of Education（教育部高可信软件技术重点实验室）；National Engineering Research Center for Software Engineering, Peking University（北京大学国家软件工程研究中心）
Image Recognition with Online Lightweight Vision Transformer: A Survey
Beijing University of Posts and Telecommunications（北京邮电大学）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology, Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science（计算能力网络与信息安全重点实验室，教育部，山东计算机科学中心，齐鲁大学，山东省计算能力互联网与服务计算重点实验室，山东省计算机科学基础研究中心）
A Closer Look at Self-Supervised Lightweight Vision Transformers
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Megvii Research（旷视研究院）；Wenzhou University（温州大学）；ShanghaiTech University（上海科技大学）
Transformer-Progressive Mamba Network for Lightweight Image Super-Resolution
Bell Honors School, Nanjing University of Posts and Telecommunications（南京邮电大学贝尔荣誉学院）；State Key Laboratory of Integrated Services Networks, Xidian University（西安电子科技大学集成服务网络国家重点实验室）；Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院模式识别与智能系统实验室）；Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院智能感知与高维信息系统教育部重点实验室）；Department of Electrical Engineering and the Institute of Communications Engineering, National Tsing Hua University（国立交通大学电子工程系与通信工程研究所）
An Experimental Study on Exploring Strong Lightweight Vision Transformers via Masked Image Modeling Pre-Training
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Megvii Research（旷视研究院）；Beijing Institute of Basic Medical Sciences（北京基础医学研究所）；Nanchang Hangkong University（南昌航空大学）；Zhejiang University of Technology（浙江工业大学）
