A Dataset for Semantic Segmentation in the Presence of Unknowns
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00386223" target="_blank" >RIV/68407700:21230/25:00386223 - isvavai.cz</a>
Result on the web
<a href="https://github.com/vojirt/benchmark_issu" target="_blank" >https://github.com/vojirt/benchmark_issu</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPR52734.2025.00142" target="_blank" >10.1109/CVPR52734.2025.00142</a>
Alternative languages
Result language
angličtina
Original language name
A Dataset for Semantic Segmentation in the Presence of Unknowns
Original language description
Before deployment in the real-world deep neural networks require thorough evaluation of how they handle both knowns, inputs represented in the training data, and unknowns (anomalies). This is especially important for scene understanding tasks with safety critical applications, such as in autonomous driving. Existing datasets allow evaluation of only knowns or unknowns - but not both, which is required to establish "in the wild" suitability of deep neural network models. To bridge this gap, we propose a novel anomaly segmentation dataset, ISSU, that features a diverse set of anomaly inputs from cluttered real-world environments. The dataset is twice larger than existing anomaly segmentation datasets, and provides a training, validation and test set for controlled in-domain evaluation. The test set consists of a static and temporal part, with the latter comprised of videos. The dataset provides annotations for both closed-set (knowns) and anomalies, enabling closed-set and open-set evaluation. The dataset covers diverse conditions, such as domain and cross-sensor shift, illumination variation and allows ablation of anomaly detection methods with respect to these variations. Evaluation results of current state-of-the-art methods confirm the need for improvements especially in domain-generalization, small and large object segmentation. The code and the dataset are available at https://github.com/vojirt/benchmark_issu.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
ISBN
979-8-3315-4364-8
ISSN
1063-6919
e-ISSN
2575-7075
Number of pages
10
Pages from-to
1439-1448
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Nashville
Event date
Jun 11, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001562507801078