A5101958388- Advanced Chemical Sensor Technologies
- Nutritional Studies and Diet
- Water Quality Monitoring Technologies
- Remote-Sensing Image Classification
- Advanced Image and Video Retrieval Techniques
- Aesthetic Perception and Analysis
- Face and Expression Recognition
- Face recognition and analysis
- Smart Agriculture and AI
- Metabolomics and Mass Spectrometry Studies
- China's Ethnic Minorities and Relations
- Generative Adversarial Networks and Image Synthesis
- Visual Attention and Saliency Detection
Nanyang Technological University
2018-2021
Ethnicity information is an integral part of human identity, and a useful identifier for various applications ranging from video surveillance, targeted advertisement to social media profiling. In recent years, Convolutional Neural Networks (CNNs) have shown state-of-the-art performance in many visual recognition problems. Currently, there are few CNN-based approaches on ethnicity classification [1], [2]. However, the suffer following limitations: (i) most face datasets do not include...
Visual food recognition on mobile devices has attracted increasing attention in recent years due to its roles individual diet monitoring and social health management analysis. Existing visual approaches usually use large server-based networks achieve high accuracy. However, these are not compact enough be deployed devices. Even though some architectures have been proposed, most of them unable obtain the performance full-size networks. In view this, this paper proposes a Joint-learning...
Food-related applications and services are essential for the health well-being of people. With rapid development social networks mobile devices, food images captured by people can offer rich knowledge about also necessary dietary assistance that require special care. Known recognition frameworks approaches in computer vision have heavy reliance on many-shot training a deep network existing large-scale datasets. However, it is common many categories difficult to collect enough training....
Brand plays a significant role in fashion clothing. Consumers are brand conscious during the clothing search and purchase. Existing visual methods [1], [8], [17]-[19] often do not explicitly consider information such as logos. logo images quite small suffer various deformations, hence pose challenge for branded search. In view of this, this paper presents new Brand-Aware Fashion Search (BAFS) framework that explores We construct dataset which consists 10K with trademark The proposed first...
Mobile visual food recognition is emerging as an important application in logging and dietary monitoring recent years. Existing methods use conventional many-shot learning to train a large backbone network, which refers the of sufficient number training data network. However, these firstly do not consider cases where certain categories have limited data. Therefore, they cannot using learning. Further, existing solutions focus on improving performance by implementing state-of-the-art full...
Visual food recognition is emerging as an important application in dietary monitoring and management recent years. Existing works use large backbone networks to achieve good performance. However, these are not able be deployed on personal portable devices due size computation cost. Some compact have been developed, however, their performance usually lower than the networks. In view of this, this paper proposes a joint distillation framework that targets high visual accuracy using network. As...