Extracting and understanding the high-level semantic information in vision and text data is considered as one of the key capabilities of effective artificial intelligence (AI) systems, which has been explored in many areas of AI, including computer vision, natural language processing, machine learning, data mining, knowledge representation, etc. Due to the success of deep representation learning, we have observed increasing research efforts in the intersection between vision and language for a better understanding of semantics, such as image captioning, visual question answering, etc. Besides, exploiting external semantic knowledge (e.g., semantic relations, knowledge graphs) for vision and text understanding also deserves more attention: The vast amount of external semantic knowledge could assist in having a “deeper” understanding of vision and/or text data, e.g., describing the contents of images in a more natural way, constructing a comprehensive knowledge graph for movies, building a dialog system equipped with commonsense knowledge, etc.
This workshop will provide a forum for researchers to review the recent progress of vision and text understanding, with an emphasis on novel approaches that involve deeper and better semantic understanding of vision and text data. The workshop is targeting a broad audience, including the researchers and practitioners in computer vision, natural language processing, machine learning, data mining, etc.
Image and Video Captioning
Visual Question Answering and Visual Dialog
Scene Graph Generation from Visual Data
Video Prediction and Reasoning
Scene Understanding
Knowledge Graph Construction
Knowledge Graph Embedding
Representation Learning
Question Answering over Knowledge Bases
Dialog Systems using Knowledge Graph
Adversarial Generation of Language & Images
Graphical Causal Models
Multimodal Representation and Fusion
Transfer Learning across Vision and Text
Three types of submissions are invited to the workshop: long papers (up to 7 pages), short papers (up to 4 pages) and demo papers (up to 4 pages).
All submissions should be formatted according to the IJCAI'2019 Formatting Instructions and Templates. Authors are required to submit their papers electronically in PDF format to the Microsoft CMT submission site.
At least one author of each accepted paper must register for the workshop, and the registration information can be found on the IJCAI-2019 website. The authors of accepted papers should present their work at the workshop.
As in previous years, IJCAI does not have a formal proceeding for workshop papers. All the accepted papers will be made available to the workshop participants.
Any question regarding paper submission, please email us: sheng.li[AT]uga.edu or yaliang.li[AT]alibaba-inc.com
Ying Shen; Jiyue Huang; Jin Zhang; Min Yang; Kai Lei
Canxiang Zhu; Zhiming Chen; Yang Liu; Juan Hu; Shujuan Sun; Bixiao Cheng; Zhendong Yang; Li Ma; Hua Chai
Jiebo Luo (University of Rochester)
Minghui Qiu; Bo Wang; Cen Chen; Xiaoyi Zeng; Jun Huang
Xin Shen; Wai Lam; Xunying Liu; Piji Li
Guojun Qi
Vijay John; Seiichi Mita
Yujia Tang
Juyang Weng
Zhihang Hu; Jason T. L. Wang
Baohua Sun; Lin Yang; Michael Lin; Wenhan Zhang; Patrick Dong; Charles Young; Jason Dong
Assistant Professor
University of Georgia
Research Scientist
Alibaba Group
Associate Professor
University at Buffalo
Professor
Northeastern University
Daoyuan Chen, Peking University
Yang Deng, Peking University
Jiashi Feng, National University of Singapore
Vishrawas Gopalakrishnan, SUNY at Buffalo
Xiaodong Jiang, University of Georgia
Chaochun Liu, JD Finance AI Lab
Jiasen Lu, Georgia Institute of Technology
Khorrami Pooya, MIT Lincoln Laboratory
Minghui Qiu, Alibaba
Huan Sun, Ohio State University
Mingming Sun, Baidu Research
Zhiqiang Tao, Northeastern University
Zhaowen Wang, Adobe Research
Chenwei Zhang, University of Illinois at Chicago
Handong Zhao, Adobe Research