| Full text | |
| Author(s): |
de Almeida, Lucas Barbosa
;
Pereira-Ferrero, Vanessa Helena
;
Valem, Lucas Pascotti
;
Almeida, Jurandy
;
Guimaraes Pedronette, Daniel Carlos
;
IEEE Comp Soc
Total Authors: 6
|
| Document type: | Journal article |
| Source: | 2021 34TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2021); v. N/A, p. 8-pg., 2021-01-01. |
| Abstract | |
Despite of the substantial success of Convolutional Neural Networks (CNNs) on many recognition and representation tasks, such models are very reliant on huge amount of data to allow effective training. In order to improve the generalization ability of CNNs, several approaches have been proposed, including variations of data augmentation strategies. With the goal of achieving more effective retrieval results on unsupervised learning scenarios, we propose a representation learning approach which exploits a rank-based formulation to build a more comprehensive data representation. The proposed model uses 2D and 3D CNNs trained by transfer learning and fuse both representations through a rank-based formulation based on manifold learning algorithms. Our approach was evaluated on an unsupervised image retrieval scenario applied to action recognition datasets. The experimental results indicated that significant effectiveness gains can be obtained on various datasets, reaching +56.93% of relative gains on MAP scores. (AU) | |
| FAPESP's process: | 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks |
| Grantee: | Daniel Carlos Guimarães Pedronette |
| Support Opportunities: | Research Grants - Young Investigators Grants - Phase 2 |
| FAPESP's process: | 20/02183-9 - Rank-based unsupervised learning through deep learning in diverse domains |
| Grantee: | Vanessa Helena Pereira Ferrero |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 17/25908-6 - Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |