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Luca Barsellotti

4 accepted papers

2025

Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Open-Vocabulary Segmentation

Luca Barsellotti, Lorenzo Bianchi, Nicola Messina, Fabio Carrara, Marcella Cornia, Lorenzo Baraldi +2
ICCV 2025poster

Open-Vocabulary Segmentation (OVS) aims at segmenting images from free-form textual concepts without predefined training classes. While existing vision-language models such as CLIP can generate segmentation masks by leveraging coarse spatial information from Vision Transformers, they face challenges…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2024

Personalized Instance-based Navigation Toward User-Specific Objects in Realistic Environments

Luca Barsellotti, Roberto Bigazzi, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
NeurIPS 2024poster

In the last years, the research interest in visual navigation towards objects in indoor environments has grown significantly. This growth can be attributed to the recent availability of large navigation datasets in photo-realistic simulated environments, like Gibson and Matterport3D. However, the na…

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2024

The Revolution of Multimodal Large Language Models: A Survey

Davide Caffagni, Federico Cocchi, Luca Barsellotti, Nicholas Moratelli, Sara Sarto, Lorenzo Baraldi +2
ACL 2024findings

Connecting text and visual modalities plays an essential role in generative intelligence. For this reason, inspired by the success of large language models, significant research efforts are being devoted to the development of Multimodal Large Language Models (MLLMs). These models can seamlessly inte…

Cited by 66Source ↗PDF ↗Scholar ↗
2024

Training-Free Open-Vocabulary Segmentation with Offline Diffusion-Augmented Prototype Generation

Luca Barsellotti, Roberto Amoroso, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
CVPR 2024poster

Open-vocabulary semantic segmentation aims at segmenting arbitrary categories expressed in textual form. Previous works have trained over large amounts of image-caption pairs to enforce pixel-level multimodal alignments. However captions provide global information about the semantics of a given imag…

Cited by 16Source ↗PDF ↗code ↗Scholar ↗