Advanced search
Start date
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Empirical Study of Multi-Task Hourglass Model for Semantic Segmentation Task

Full text
Saire, Darwin [1] ; Rivera, Adin Ramirez [1]
Total Authors: 2
[1] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas - Brazil
Total Affiliations: 1
Document type: Journal article
Source: IEEE ACCESS; v. 9, p. 80654-80670, 2021.
Web of Science Citations: 0

The semantic segmentation (SS) task aims to create a dense classification by labeling at the pixel level each object present on images. Convolutional neural network (CNN) approaches have been widely used, and exhibited the best results in this task. However, the loss of spatial precision on the results is a main drawback that has not been solved. In this work, we propose to use a multi-task approach by complementing the semantic segmentation task with edge detection, semantic contour, and distance transform tasks. We propose that by sharing a common latent space, the complementary tasks can produce more robust representations that can enhance the semantic labels. We explore the influence of contour-based tasks on latent space, as well as their impact on the final results of SS. We demonstrate the effectiveness of learning in a multi-task setting for hourglass models in the Cityscapes, CamVid, and Freiburg Forest datasets by improving the state-of-the-art without any refinement post-processing. (AU)

FAPESP's process: 19/18678-0 - Semantic Segmentation Using Hourglass Learning Model
Grantee:Darwin Danilo Saire Pilco
Support type: Scholarships abroad - Research Internship - Doctorate
FAPESP's process: 19/07257-3 - Learning representations through deep generative models on video
Grantee:Gerberth Adín Ramírez Rivera
Support type: Regular Research Grants
FAPESP's process: 17/16597-7 - Semantic Segmentation on Videos
Grantee:Darwin Danilo Saire Pilco
Support type: Scholarships in Brazil - Doctorate