| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910755098903321 |
|
|
Autore |
Ji Wei |
|
|
Titolo |
Proceedings of the 1st International Workshop on Deep Multimodal Learning for Information Retrieval / / Wei Ji, [and four others], SIGMM |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
New York, NY, USA : , : Association for Computing Machinery, , 2023 |
|
|
|
|
|
|
|
Descrizione fisica |
|
1 online resource (75 pages) |
|
|
|
|
|
|
Collana |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Computer science |
Neural networks |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Sommario/riassunto |
|
It is our great pleasure to welcome you to the 2023 ACM Multimedia Workshop - MMIR 2023. The emergence of multimodal learning offers a feasible way for multimodal IR. Within recent decades with the rapid development of deep learning techniques, the triumph of multimodal learning has been witnessed. Deep multimodal learning has been defined as to use of deep neural techniques to model and learn from multiple sources of data or modalities among others. In the context of IR, deep multimodal learning has shown great potential to improve the performance and application scope of retrieval systems, i.e., by enabling better understanding and processing of the diverse types of data. MMIR'23 workshop can be a good complementarity to place the major focus on multimodal IR. This workshop sets the goal to extend existing work in this direction, by bringing together and facilitating the community of researchers and practitioners. And meanwhile, we aim to encourage an exchange of perspectives and solutions between industry and academia to bridge the gap between academic design guidelines and the best practices in the industry regarding multimodal IR. |
|
|
|
|
|
|
|
| |