ViT

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
PDF Cite Code
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
PDF Cite
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
Cite
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
PDF Cite Video
Investigating Bidimensional Downsampling in Vision Transformer Models