Image segmentation losses with modules expressing a relationship between predictions

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F22%3AA2302FC5" target="_blank" >RIV/61988987:17610/22:A2302FC5 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-031-08974-9_27" target="_blank" >http://dx.doi.org/10.1007/978-3-031-08974-9_27</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-08974-9_27" target="_blank" >10.1007/978-3-031-08974-9_27</a>

Result language
angličtina
Original language name
Image segmentation losses with modules expressing a relationship between predictions
Original language description
We focus on semantic image segmentation with the usage of deep neural networks and give emphasis on the loss functions used for training the networks. Considering region-based losses, Dice loss, and Tversky loss, we propose two independent modules that easily modify the loss functions to take into account the relationship between the class predictions and increase the slope of the gradient. The first module expresses the ambiguity between classes and the second module utilizes a differentiable soft argmax function. Each of the modules is used before the standard loss is computed and remains untouched. In the benchmark, we involved two neural network architectures with two different backbones, selected two loss functions, and examined separately two scenarios for softmax and sigmoid top activation functions. In the experiment, we demonstrate the usefulness of our modules by improving the IOU and F1 coefficients on the test dataset for all scenarios tested. Moreover, the usage of the modules decreases overfitting. The proposed modules are easy to integrate into existing solutions and add near-zero computational overhead.
Czech name
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Czech description
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Project
<a href="/en/project/EF17_049%2F0008414" target="_blank" >EF17_049/0008414: Centre for the development of Artificial Intelligence Methods for the Automotive Industry of the region</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Information Processing and Management of Uncertainty in Knowledge-Based Systems
ISBN
978-3-031-08973-2
ISSN
1865-0929
e-ISSN
1865-0937
Number of pages
12
Pages from-to
343-354
Publisher name
Springer
Place of publication
Milano
Event location
Milano
Event date
Jul 11, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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