| Full text | |
| Author(s): |
Saire, Darwin
;
Rivera, Adin Ramirez
Total Authors: 2
|
| Document type: | Journal article |
| Source: | IEEE ACCESS; v. 10, p. 14-pg., 2022-01-01. |
| Abstract | |
The semantic segmentation task aims at dense classification at the pixel-wise level. Deep models exhibited progress in tackling this task. However, one remaining problem with these approaches is the loss of spatial precision, often produced at the segmented objects' boundaries. Our proposed model addresses this problem by providing an internal structure for the feature representations while extracting a global representation that supports the former. To fit the internal structure, during training, we predict a Gaussian Mixture Model from the data, which, merged with the skip connections and the decoding stage, helps avoid wrong inductive biases. Furthermore, our results show that we can improve semantic segmentation by providing both learning representations (global and local) with a clustering behavior and combining them. Finally, we present results demonstrating our advances in Cityscapes and Synthia datasets. (AU) | |
| FAPESP's process: | 19/07257-3 - Learning representations through deep generative models on video |
| Grantee: | Gerberth Adín Ramírez Rivera |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 17/16597-7 - Semantic Segmentation on Videos |
| Grantee: | Darwin Danilo Saire Pilco |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 19/18678-0 - Semantic Segmentation Using Hourglass Learning Model |
| Grantee: | Darwin Danilo Saire Pilco |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |