Roberto Amoroso
Roberto Amoroso
Home
News
Experience
Awards
Publications
Activities
Contact
Light
Dark
Automatic
ViT
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
Cite
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
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
Cite
×