[CFP] IJCAI 2019 Workshop on Bringing Semantic Knowledge into Vision and Text Understanding

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Sheng Li

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Mar 5, 2019, 9:19:38 PM3/5/19
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Call For Papers

The First International Workshop on Bringing Semantic Knowledge into Vision and Text Understanding

@IJCAI-2019, August 10-16, Macao, China


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 one-day 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 a deeper and better semantic understanding of version 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. 

This workshop will include several invited talks and peer-reviewed papers (oral and poster presentations). We encourage submissions on a variety of research topics. The topics of interest include (but not limited to): 
(1). Image and Video Captioning 
(2). Visual Question Answering and Visual Dialog 
(3). Scene Graph Generation from Visual Data 
(4). Video Prediction and Reasoning 
(5). Scene Understanding 
(6). Knowledge Graph Construction 
(7). Knowledge Graph Embedding 
(8). Representation Learning 
(9). KBQA: Question Answering over Knowledge Bases 
(10). Dialog Systems using Knowledge Graph 
(11). Adversarial Generation of Nature Language and Images 
(12). Transfer Learning and Domain Adaptation across Vision and Text 
(13). Graphical Causal Models 
(14). Multimodal Representation and Fusion

**Important Dates
Submission Deadline: April 15, 2019 
Notification: May 10, 2019 
Camera Ready: June 1, 2019

**Organizers
Sheng Li, University of Georgia, Athens, GA, USA. 
Yaliang Li, Alibaba Group, Bellevue, WA, USA 
Jing Gao, University at Buffalo, Buffalo, NY, USA 
Yun Fu, Northeastern University, Boston, MA, USA


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