CA 2019 - International Workshop on Cross-media Analysis for Semantic Knowledge Reasoning and Transfer

Organizers
Yang Yang (dlyang-AT-gmail.com), University of Electronic Science and Technology of China, China.
Yang Wang (yang.wang-AT-dlut.edu.cn), Dalian University of Technology, China.
Xing Xu (xing.xu-AT-uestc.edu.cn), University of Electronic Science and Technology of China, China.
Zi Huang (zi.huang-AT-uq.edu.au), The University of Queensland, Australia.
Description
Due to the explosive growth of multi-modal data (e.g., images, videos, blogs, sensor data, etc) on the Internet, together with the urgent requirement of joint understanding the heterogeneous data, the research attention over multi-modal data, especially the visual and textual data to bridge the semantic gap has attracted a huge amount of interest from the computer vision and natural language processing communities. Great efforts have been made to study the intersection of cross-media data, especially combining vision and language, and fantastic applications include (i) generating image descriptions using natural language, (ii) visual question answering, (iii) retrieval of images based on textural queries (and vice versa), (iv) generating images/videos from textual descriptions, (v) language grounding and many other related topics.
Though booming recently, it remains challenging as reasoning of the connections between visual contents and linguistic words are difficult. Semantic knowledge involves reasoning the external knowledge of the word. Although reasoning ability is always claimed in recent studies, most “reasoning” simply uncovers latent connections between visual elements and textual/semantic facts during the training on manually annotated datasets with a large number of image-text pairs. Furthermore, recent studies are always specific to certain datasets that lack generalization ability, i.e., the semantic knowledge obtained from specific dataset cannot be directly transferred to other datasets, as different benchmark may have different characteristics of its own. One potential solution is leveraging external knowledge resources (e.g., social-media sites, expert systems and Wikipedia.) as intermediate bridge for knowledge transfer. However, it is still implicit that how to appropriately incorporate the comprehensive knowledge resources for more effective knowledge-based reasoning and transfer across datasets. Towards a broad perspective of applications, integrating vision and language for knowledge reasoning and transfer has yet been well exploited in existing research.
Scope and Topics
This special issue targets the researchers and practitioners from both the academia and industry to explore recent advanced learning models and systems that address the challenges in cross-media analysis. It provides a forum to publish recent state-of-the-art research findings, methodologies, technologies and services in vision-language technology for practical applications. We invite original and high quality submissions addressing all aspects of this field, which is closely related to multimedia search, multi-modal learning, cross-media analysis, cross-knowledge transfer and so on.
Topics of interest include, but are not limited to:
Deep learning methods for language and vision
Generative adversarial networks for cross-modal data analysis
Big data storage, indexing, and searching over multi-modal data
Transfer learning for vision and language
Cross-media analysis (retrieval, hashing, reasoning, etc)
Multi-modal learning and semantic representation learning
Learning multi-graph over cross-modal data
Generating image/video descriptions using natural language
Visual question answering/generation on images or videos
Retrieval of images based on textural queries (and vice versa)
Generating images/videos from textual descriptions
Language grounding