A5090052670- Face and Expression Recognition
- Emotion and Mood Recognition
- Advanced Neural Network Applications
- Face recognition and analysis
- Advanced Computing and Algorithms
- Domain Adaptation and Few-Shot Learning
- Geotechnical Engineering and Soil Mechanics
- Brain Tumor Detection and Classification
- Geotechnical Engineering and Underground Structures
- Video Surveillance and Tracking Methods
- Advanced Image and Video Retrieval Techniques
- Network Security and Intrusion Detection
- IoT and Edge/Fog Computing
- Geotechnical Engineering and Soil Stabilization
- Hand Gesture Recognition Systems
- Biometric Identification and Security
- Neural Networks Stability and Synchronization
- Network Time Synchronization Technologies
- Image Enhancement Techniques
- Grouting, Rheology, and Soil Mechanics
- Cybersecurity and Information Systems
- Aesthetic Perception and Analysis
- Stability and Control of Uncertain Systems
- Radiation Effects in Electronics
- Geotechnical Engineering and Analysis
University of Electronic Science and Technology of China
2020-2025
Chengdu University
2025
Tongji University
2021
Current state-of-the-art medical image segmentation methods prioritize accuracy but often at the expense of increased computational demands and larger model sizes. Applying these large-scale models to relatively limited scale datasets tends induce redundant computation, complicating process without necessary benefits. This approach not only adds complexity also presents challenges for integration deployment lightweight on edge devices. For instance, recent transformer-based have excelled in...
Existing facial expression recognition (FER) methods are mainly devoted to learning discriminative features from normal-light images. However, their performance drops sharply when they used for low-light In this paper, we propose a novel FER framework (termed LL-FER) that can simultaneously enhance the images and tasks of Specifically, first meticulously design enhancement network (LLENet) recover expressions images' rich detail information. Then, joint loss train LLENet with in cascade...
<title>Abstract</title> With the innovation in computer vision, facial expression recognition (FER) is a dynamic research domain considering extensive practical applications various domains together with health, education, safety, law enforcement, Banking, marketing, and many more. The researchers have conducted tremendous work on basic expressions recognition, but less compound emotions which complex features due to combination of emotions. Different deep learning models been used for...
Recent advanced research shows that deep learning has great potential in facial expression recognition, specially, the basic recognition field, many researchers have proposed lots of networks with excellent performance. As for compound expression, although it value, there are only few researches existed, and their performance is not satisfactory. So, still a lot work to be done improve recognition. To address this problem, we introduce transfer solution where design fine-tuning structure...
Abstract Mine water inrush is affected by many factors such as geological structure and fracture zone. However, there may be overlap among these factors, leading to uncertainty, fuzzy similarity nonlinear relationship most of them. Therefore, the traditional mathematical model not ideal predict inrush. This paper proposes an intelligent for predicting from coal floor based on simulated annealing particle swarm optimization-extreme learning machine (SAPSO-ELM). Based 144 groups data 36...
Facial expression recognition (FER) is vital for human-computer interaction and emotion analysis, yet recognizing expressions in low-resolution images remains challenging. This paper introduces a practical method called Dynamic Resolution Guidance Expression Recognition (DRGFER) to effectively recognize facial with varying resolutions without compromising FER model accuracy. Our framework comprises two main components: the Network (RRN) Multi-Resolution Adaptation (MRAFER). The RRN...
With the cutting-edge advancements in computer vision, facial expression recognition (FER) is an active research area due to its broad practical applications. It has been utilized various fields, including education, advertising and marketing, entertainment gaming, health, transportation. The recognition-based systems are rapidly evolving new challenges, significant studies have conducted on both basic compound expressions of emotions; however, measuring emotions challenging. Fueled by...