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PanDA: Panoptic Data Augmentation

Liu, Yang and Perona, Pietro and Meister, Markus (2019) PanDA: Panoptic Data Augmentation. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20200526-134149667

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Abstract

The recently proposed panoptic segmentation task presents a significant challenge of image understanding with computer vision by unifying semantic segmentation and instance segmentation tasks. In this paper we present an efficient and novel panoptic data augmentation (PanDA) method which operates exclusively in pixel space, requires no additional data or training, and is computationally cheap to implement. By retraining original state-of-the-art models on PanDA augmented datasets generated with a single frozen set of parameters, we show robust performance gains in panoptic segmentation, instance segmentation, as well as detection across models, backbones, dataset domains, and scales. Finally, the effectiveness of unrealistic-looking training images synthesized by PanDA suggest that one should rethink the need for image realism for efficient data augmentation.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/1911.12317arXivDiscussion Paper
ORCID:
AuthorORCID
Perona, Pietro0000-0002-7583-5809
Meister, Markus0000-0003-2136-6506
Subject Keywords:Data Augmentation, Panoptic Segmentation, Scene Understanding, Image Synthesis
Record Number:CaltechAUTHORS:20200526-134149667
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200526-134149667
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:103461
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:26 May 2020 22:54
Last Modified:26 May 2020 22:54

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