EMCLR '24: 1st International Workshop on Efficient Multimedia Computing under Limited Resources
Cutting-edge multimedia computing techniques, especially those recognized for their large-scale capabilities, have exhibited impressive achievements in diverse fields such as computer vision, natural language processing, and speech recognition. However, the significant reliance on vast amounts of data, label, and computational resources of these multimedia computing techniques present challenges when deploying to real-world scenarios with limited resources. This workshop aims to bring together experts in data-efficient, label-efficient, and computation-efficient multimedia computing fields, establishing a collaborative platform to exchange recent breakthroughs and deliberate on the future direction of multimedia computing models. By facilitating the exchange of ideas and insights, we aspire to address the efficiency challenges inherent in multimedia computing and contribute to the evolution of its practical applications in the real world.
Authors
- Xianglong Liu (ORCID: https://orcid.org/0000-0001-8425-4195)
- Yuqing Ma (ORCID: https://orcid.org/0009-0001-4216-9133)
- Jinyang Guo (ORCID: https://orcid.org/0000-0003-1956-3367)
- Zhenghao Chen (ORCID: https://orcid.org/0000-0003-0155-4462)
- Dacheng Tao (ORCID: https://orcid.org/0000-0001-7225-5449)
- Wanli Ouyang (ORCID: https://orcid.org/0000-0002-9163-2761)
- Jinman Kim (ORCID: https://orcid.org/0000-0001-5960-1060)
Institutions
- The University of Sydney (AU)
- Nanyang Technological University (SG)
- Shanghai Artificial Intelligence Laboratory (CN)
- University of Newcastle Australia (AU)
- Beihang University (CN)
Publication Details
- Published
- 2024-10-26
- DOI
- https://doi.org/10.1145/3688863.3696341
- Primary Topic
- Advanced Image and Video Retrieval Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00