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Contribution to Panoptic Segmentation

Manuel Alejandro Diaz-Zapata 1, 2
1 CHROMA - Robots coopératifs et adaptés à la présence humaine en environnements dynamiques
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
Abstract : Full visual scene understanding has always been one of the main goals of machine perception. The ability to describe the components of a scene using only information taken by a digital camera has been the main focus of computer vision tasks such as semantic segmentation and instance segmentation. Where by using Deep Learning techniques, a neural network is capable to assign a label to each pixel of an image (semantic segmentation) or define the boundaries of an instance or object with more precision than a bounding box (instance segmentation). The task of Panoptic Segmentation tries to achieve a full scene description by merging semantic and instance segmentation information and leveraging the strengths of these two tasks. On this report it is shown a possible alternative to solve this merging problem by using Convolutional Neural Networks (CNNs) to refine the boundaries between each class.
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Contributor : Manuel Diaz Zapata Connect in order to contact the contributor
Submitted on : Tuesday, February 9, 2021 - 2:15:05 PM
Last modification on : Wednesday, November 3, 2021 - 3:58:53 AM


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  • HAL Id : hal-02300774, version 2


Manuel Alejandro Diaz-Zapata. Contribution to Panoptic Segmentation. [Technical Report] RT-0506, Inria; Universidad Autónoma de Occidente. 2019. ⟨hal-02300774v2⟩



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