HIM 2019 - The Third Workshop on Human Identification in Multimedia (HIM'19)

Organizers
Liangliang Ren (Department of Automation University of Tsinghua University)
Guangyi Chen (Dept. of Automation University of Tsinghua University)
Dr. Jiwen Lu (Contact Person)(lujiwen-AT-tsinghua.edu.cn), Department of Automation Tsinghua University, China
Description
Human Identities are an important information source in many high-level multimedia analysis tasks such as video summarization, semantic retrieval, interaction indexing, and scene understanding. The aim of this workshop is to bring together researchers in computer vision and multimedia to share ideas and propose solutions on how to address the many open issues in human identification, and present new datasets that introduce new challenges in the field. Human identification in multimedia is one relatively new problem in multimedia analysis and, recently, it has attracted the attention of many researchers in the field. Human Identification is significant to many multimedia related applications such as video surveillance, video search, human-computer interaction, and video summarization. Recent advances in feature representations, modeling, and inference techniques have led to a significant progress in the field. The proposed workshop aims to explore recent progress in human identification with multimedia data by taking stock of the past five years of work in this field and evaluating different algorithms. The proposed workshop will help the community to understand the challenges and opportunities of human identification in multimedia techniques for the next few years.
Scope and Topics
Topics of interests include (but not limited to) the following streams:
Multimedia feature representation
Image feature representation
Video feature representation
Audio feature representation
Multiview feature representation
Multimodal feature representation
Statistical learning for human identification
Sparse learning for human identification
Dictionary learning for human identification
Manifold learning for human identification
Metric learning for human identification
Deep learning for human identification
Applications
Video surveillance
Multimedia search
Video summarization
Benchmark datasets
Comparative evaluations