SPECIAL SESSION

Special Session Ⅰ: Algorithm Optimization and Applications Based on Machine Learning and Deep Learning

Session Chairs: Assoc. Prof. Lifeng Yin, Dalian Jiaotong University, China

Assoc. Prof. Miao Wang, Henan University of Engineering, China

Keywords: Supervised Learning; Unsupervised Learning; Reinforcement Learning; Deep Learning

Information: Algorithm optimization and applications based on machine learning and deep learning are important research directions in the field of modern artificial intelligence, aimed at enhancing model performance and computational efficiency. Optimization methods include hyperparameter tuning, feature selection, training process optimization, and model compression, all designed to improve the accuracy and responsiveness of algorithms. These optimization techniques are widely applied in areas such as natural language processing, computer vision, healthcare, and fintech, contributing to higher predictive accuracy and real-time decision-making capabilities.

Below is an incomplete list of potential topics to be covered in the Special Session:

  • Optimization and Applications of Supervised Learning Algorithms  

  • Optimization and Applications of Unsupervised Learning Algorithms  

  • Optimization and Applications of Reinforcement Learning Algorithms  

  • Optimization and Applications Based on YOLOv8 Algorithm  

  • Optimization and Applications Based on Graph Neural Networks  

  • Research on the Application of Deep Learning in Multimodal Data Fusion

Submission Deadline: December 20, 2025


Special Session Ⅱ: Advances in Human Motion and Activity Analysis

Session Chairs: Prof. Xianglei Xing, Harbin Engineering University, China


Assoc. Prof. Mingliang Gao, Shandong University of Technology, China

Keywords: Gait Recognition; People Counting; Pedestrian Tracking; Behavior Analysis; Trajectory Prediction; Generative Models

Information: This session explores the latest advancements in human motion and activity analysis, covering a wide range of topics such as gait recognition, people counting, pedestrian tracking, behavior analysis, trajectory prediction, and generative models. These technologies are crucial in various applications, including surveillance, smart cities, human-computer interaction, and robotics. The session will delve into innovative methods for recognizing and tracking human movement, analyzing behaviors, and predicting trajectories, as well as generating realistic human models. Researchers and practitioners from these domains will share their latest findings, techniques, and applications in a collaborative environment.


The topics of this special session will include but are not limited to:

  • Gait Recognition and Analysis: Advances in techniques for identifying and analyzing human gait patterns for security, healthcare, and other applications.

  • People Counting and Detection: Methods and technologies for counting individuals in crowded or dynamic environments using computer vision and machine learning.

  • Pedestrian Tracking: Techniques for tracking the movement of pedestrians in real-time, including multi-object tracking and occlusion handling.

  • Behavior Analysis and Recognition: Approaches to analyze and recognize human behavior, including emotion detection, activity classification, and action recognition.

  • Trajectory Prediction: Predicting the future movement paths of individuals or groups based on historical data and real-time observations.

  • Generative Models for Human Motion: Using generative models, such as GANs, Energy-based models and Diffusion model, to create realistic human motion for

    virtual environments, animation, and robotics.

Submission Deadline: December 20, 2025


Special Session Ⅲ: Scene Perception and Reconstruction for Autonomous Driving

Session Chair: Assoc. Prof. Yuanzhouhan Cao, Beijing Jiaotong University, China

Keywords: Autonomous Driving; Multi-source Data Fusion;Dynamic Scene Perception; 3D Scene Reconstruction

Information: Autonomous driving is at the core of intelligent transportation development. It not only has the potential to alleviate traffic congestion, reduce accidents, and improve travel efficiency, but also represents a critical step toward building smart cities and achieving sustainable transportation. Currently, autonomous driving technology is rapidly advancing toward higher levels of automation; however, numerous challenges still stand in the way of fully autonomous driving. Particularly in complex, dynamic environments, achieving high-precision perception, real-time responsiveness, and environment reconstruction requires overcoming significant technical hurdles such as efficient multi-source data fusion, real-time dynamic perception, and addressing environmental uncertainty.


This session will cover a range of key technologies, from multi-sensor data fusion to 3D scene reconstruction. Specific topics include object detection, tracking, segmentation, multi-view fusion, dynamic environment perception, real-time high-precision map updates, and 3D scene understanding and reconstruction. Additionally, the session will address challenges related to system robustness, real-time performance, and adaptability in complex driving environments, exploring the feasibility and prospects of various solution strategies. The aim of this session is to provide a platform for experts and practitioners from academia and industry to foster technical exchange and collaborative innovation, collectively advancing autonomous driving technology.

Scope of Submissions includes but is not limited to:

  • Multi-sensor Fusion

  • Object Detection and Tracking

  • Scene Understanding and Segmentation

  • Collaborative Perception

  • Transfer Learning

  • Reinforcement Learning

  • End-to-End Learning

  • High-Precision Map Construction

  • 3D Scene Reconstruction

  • Simulation Testing and Validation

Submission Deadline: December 20, 2025