| Texto completo | |
| Autor(es): |
Saire, Darwin
;
Rivera, Adin Ramirez
Número total de Autores: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | IEEE ACCESS; v. 10, p. 14-pg., 2022-01-01. |
| Resumo | |
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) | |
| Processo FAPESP: | 19/07257-3 - Aprendendo representações através de modelos generativos profundos em vídeo |
| Beneficiário: | Gerberth Adín Ramírez Rivera |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 17/16597-7 - Segmentação Semântica em Vídeos |
| Beneficiário: | Darwin Danilo Saire Pilco |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |
| Processo FAPESP: | 19/18678-0 - Segmentação Semântica Usando um Modelo de Aprendizagem Hourglass |
| Beneficiário: | Darwin Danilo Saire Pilco |
| Modalidade de apoio: | Bolsas no Exterior - Estágio de Pesquisa - Doutorado |