Semantic

FreeDA: Training-Free Open-Vocabulary Segmentation with Offline Diffusion-Augmented Prototype Generation
[ CVPR 2024 ] We present FreeDA, a novel training-free diffusion-augmented method for open-vocabulary segmentation, which leverages diffusion models to visually localize generated concepts and local-global similarities to match superpixel-based class-agnostic regions with semantic classes.
Luca Barsellotti, Roberto Amoroso, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
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FreeDA: Training-Free Open-Vocabulary Segmentation with Offline Diffusion-Augmented Prototype Generation
FOSSIL: Free Open-Vocabulary Semantic Segmentation through Synthetic References Retrieval
[ WACV 2024 ] We present FOSSIL, a novel Unsupervised Open-Vocabulary Semantic Segmentation model that enables a self-supervised visual backbone to perform open-vocabulary segmentation directly on the visual modality by retrieving a support set of generated synthetic references.
Luca Barsellotti, Roberto Amoroso, Lorenzo Baraldi, Rita Cucchiara
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FOSSIL: Free Open-Vocabulary Semantic Segmentation through Synthetic References Retrieval
What’s Outside the Intersection? Fine-grained Error Analysis for Semantic Segmentation Beyond IoU
[ WACV 2024 ] We present a novel method for enhancing semantic segmentation models evaluation by categorizing errors, offering insights into false positives/negatives, and improving performance through the combination of model strengths.
Maximilian Bernhard, Roberto Amoroso, Yannic Kindermann, Lorenzo Baraldi, Rita Cucchiara, Volker Tresp, Matthias Schubert
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What’s Outside the Intersection? Fine-grained Error Analysis for Semantic Segmentation Beyond IoU
Superpixel Positional Encoding to Improve ViT-based Semantic Segmentation Models
[ BMVC 2023 ] We present a novel superpixel-based positional encoding technique that combines Vision Transformer (ViT) features with superpixels priors to improve the performance of semantic segmentation architectures.
Roberto Amoroso, Matteo Tomei, Lorenzo Baraldi, Rita Cucchiara
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Superpixel Positional Encoding to Improve ViT-based Semantic Segmentation Models