VE 2019 - International Workshop on Visual Emotion Analysis: Theories and Applications

Organizers
Prof. Lifang Wu (lfwu-AT-bjut.edu.cn), Beijing University of Technology, China.
Prof. Jufeng Yang (yangjufeng-AT-nankai.edu.cn), Nankai University, China.
Prof. Rongrong Ji (rrji-AT-xmu.edu.cn), Xiamen University, China.
Description
With the rapid development of digital photography technology and widespread popularity of social networks, people tend to express their opinions using images and videos together with text, resulting in a large volume of visual content. To manage, recognize and retrieve such gigantic visual collections poses significant technical challenges. Visual emotion analysis of the large-scale visual content is rather challenging because it involves multidisciplinary understanding of human perception and behavior. The development is constrained mainly by the affective gap and the subjectivity of emotion perceptions. Recently, great advancements in machine learning and artificial intelligence have made large-scale affective computing of visual content a possibility, which received a lot of interest and attention from both academic and industrial research communities.
Scope and Topics
This workshop seeks original contributions reporting the most recent progress on different research directions and methodologies on visual emotion recognition and retrieval, as well as the wide applications. It targets a mixed audience of researchers and product developers from the multimedia community, and may draw attention of people in machine learning, psychology, computer vision, etc. The topics of interest include, but are not limited to:
Dominant emotion recognition
Discrete emotion distribution estimation
Continuous emotion distribution estimation
Personalized emotion perception prediction
Group emotion clustering and affective region detection
Weakly-supervised/unsupervised learning for emotion recognition
Few/one shot learning for emotion recognition
Deep learning and reinforcement learning for emotion recognition
Metric learning for emotion recognition
Multi-modal/multi-task learning for emotion recognition
Image retrieval incorporating emotion
Emotion based visual content summarization
Image captioning with emotion
Virtual reality, such as affective human-computer interaction
Applications in entertainment, education, psychology, and health care,
etc.