The 2nd International Workshop on Deep Multi-modal Generation and Retrieval

Information generation (IG) and information retrieval (IR) are two key representative approaches of information acquisition, i.e., producing content either via generation or via retrieval. While traditional IG and IR have achieved great success within the scope of languages, the under-utilization of varied data sources in different modalities (i.e., text, images, audio, and video) would hinder IG and IR techniques from giving the full advances and thus limits the applications in the real world. Knowing the fact that our world is replete with multimedia information, this special issue encourages the development of deep multimodal learning for the research of IG and IR. Benefiting from a variety of data types and modalities, some latest prevailing techniques are extensively invented to show great facilitation in multimodal IG and IR learning, such as DALL-E, Stable Diffusion, GPT4, Sora, etc. Given the great potential shown by multimodal-empowered IG and IR, there can be still unsolved challenges and open questions in these directions. With this workshop, we aim to encourage more explorations in Deep Multimodal Generation and Retrieval, providing a platform for researchers to share insights and advancements in this rapidly evolving domain.

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Publication Details

Published
2024-10-23
DOI
https://doi.org/10.1145/3689091.3690093
Primary Topic
Multimodal Machine Learning Applications
Type
article
Field-Weighted Citation Impact
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The 2nd International Workshop on Deep Multi-modal Generation and Retrieval

Yueting Zhuang, Juncheng Li, Zhedong Zheng, Yinwei Wei et al.
Multimodal Machine Learning Applications
article

The 2nd International Workshop on Deep Multi-modal Generation and Retrieval

Yueting Zhuang, Juncheng Li, Zhedong Zheng, Yinwei Wei, Hao Fei, Roger Zimmermann, Lizi Liao, Long Chen, Wei Ji
article en

Abstract

Information generation (IG) and information retrieval (IR) are two key representative approaches of information acquisition, i.e., producing content either via generation or via retrieval. While traditional IG and IR have achieved great success within the scope of languages, the under-utilization of varied data sources in different modalities (i.e., text, images, audio, and video) would hinder IG and IR techniques from giving the full advances and thus limits the applications in the real world. Knowing the fact that our world is replete with multimedia information, this special issue encourages the development of deep multimodal learning for the research of IG and IR. Benefiting from a variety of data types and modalities, some latest prevailing techniques are extensively invented to show great facilitation in multimodal IG and IR learning, such as DALL-E, Stable Diffusion, GPT4, Sora, etc. Given the great potential shown by multimodal-empowered IG and IR, there can be still unsolved challenges and open questions in these directions. With this workshop, we aim to encourage more explorations in Deep Multimodal Generation and Retrieval, providing a platform for researchers to share insights and advancements in this rapidly evolving domain.

National University of Singapore (SG), Hong Kong University of Science and Technology (HK), University of Macau (MO), Monash University (AU), Zhejiang University (CN), Singapore Management University (SG), University of Hong Kong (HK)
Openalex Percentile: Top 82%
Multimodal Machine Learning Applications
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The 2nd International Workshop on Deep Multi-modal Generation and Retrieval — Yueting Zhuang, Juncheng Li, et al. · (2024) | TGRS Research Map | TGRS