Vision Transformer

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
Enhancing Open-Vocabulary Semantic Segmentation with Prototype Retrieval
[ ICIAP 2023 ] We propose a novel open-vocabulary semantic segmentation paradigm based on weakly supervised visual prototypes extracted from image-caption pairs and adopt a retrieval-based approach to combine visual and textual features to enhance segmentation performance.
Luca Barsellotti, Roberto Amoroso, Lorenzo Baraldi, Rita Cucchiara
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Enhancing Open-Vocabulary Semantic Segmentation with Prototype Retrieval
Investigating Bidimensional Downsampling in Vision Transformer Models
[ ICIAP 2021 | Best Paper Award sponsored by NVIDIA ] We explore the application of a 2D max-pooling operator to improve the efficiency of Transformer-based architecture for classification.
Paolo Bruno, Roberto Amoroso, Marcella Cornia, Silvia Cascianelli, Lorenzo Baraldi, Rita Cucchiara
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Investigating Bidimensional Downsampling in Vision Transformer Models