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• W4385805000 abstract "Most dense recognition approaches bring a separate decision in each particular pixel. These approaches deliver competitive performance in usual closed-set setups. However, important applications in the wild typically require strong performance in presence of outliers. We show that this demanding setup greatly benefits from mask-level predictions, even in the case of non-finetuned baseline models. Moreover, we propose an alternative formulation of dense recognition uncertainty that effectively reduces false positive responses at semantic borders. The proposed formulation produces a further improvement over a very strong baseline and sets the new state of the art in outlier-aware semantic segmentation with and without training on negative data. Our contributions also lead to performance improvement in a recent panoptic setup. In-depth experiments confirm that our approach succeeds due to implicit aggregation of pixel-level cues into mask-level predictions." @default.
• W4385805000 created "2023-08-15" @default.
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• W4385805000 date "2023-06-01" @default.
• W4385805000 modified "2023-09-26" @default.
• W4385805000 title "On Advantages of Mask-level Recognition for Outlier-aware Segmentation" @default.
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• W4385805000 doi "https://doi.org/10.1109/cvprw59228.2023.00295" @default.
• W4385805000 hasPublicationYear "2023" @default.
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