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Simone Magistri

6 accepted papers

2026

IsoCLIP: Decomposing CLIP Projectors for Efficient Intra-modal Alignment

Simone Magistri, Dipam Goswami, Marco Mistretta, Bartłomiej Twardowski, Joost van de Weijer, Andrew D. Bagdanov
CVPR 2026

Vision-Language Models like CLIP are extensively used for inter-modal tasks which involve both visual and text modalities. However, when the individual modality encoders are applied to inherently intra-modal tasks like image-to-image retrieval, their performance suffers from the intra-modal misalign

Cited by 0Source ↗code ↗Scholar ↗
2026

SpectralGCD: Spectral Concept Selection and Cross-modal Representation Learning for Generalized Category Discovery

Lorenzo Caselli, Marco Mistretta, Simone Magistri, Andrew D. Bagdanov
ICLR 2026poster

Generalized Category Discovery (GCD) aims to identify novel categories in unlabeled data while leveraging a small labeled subset of known classes. Training a parametric classifier solely on image features often leads to overfitting to old classes, and recent multimodal approaches improve performance…

Cited by 0Source ↗code ↗Scholar ↗
2025

Accurate and Efficient Low-Rank Model Merging in Core Space

Aniello Panariello, Daniel Marczak, Simone Magistri, Angelo Porrello, Bartłomiej Twardowski, Andrew D. Bagdanov +2
NeurIPS 2025poster

In this paper, we address the challenges associated with merging low-rank adaptations of large neural networks. With the rise of parameter-efficient adaptation techniques, such as Low-Rank Adaptation (LoRA), model fine-tuning has become more accessible. While fine-tuning models with LoRA is highly e…

Cited by 0Source ↗code ↗Scholar ↗
2025

Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learning

Dipam Goswami, Simone Magistri, Kai Wang, Bartłomiej Twardowski, Andrew D. Bagdanov, Joost van de Weijer
NeurIPS 2025poster

Using pre-trained models has been found to reduce the effect of data heterogeneity and speed up federated learning algorithms. Recent works have explored training-free methods using first- and second-order statistics to aggregate local client data distributions at the server and achieve high perform…

Cited by 0Source ↗code ↗Scholar ↗
2025

No Task Left Behind: Isotropic Model Merging with Common and Task-Specific Subspaces

Daniel Marczak, Simone Magistri, Sebastian Cygert, Bartłomiej Twardowski, Andrew D. Bagdanov, Joost van de Weijer
ICML 2025poster

Model merging integrates the weights of multiple task-specific models into a single multi-task model. Despite recent interest in the problem, a significant performance gap between the combined and single-task models remains. In this paper, we investigate the key characteristics of task matrices --…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2024

Elastic Feature Consolidation For Cold Start Exemplar-Free Incremental Learning

Simone Magistri, Tomaso Trinci, Albin Soutif, Joost van de Weijer, Andrew D. Bagdanov
ICLR 2024poster

Exemplar-Free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data. In this paper, we consider the challenging Cold Start scenario in which insufficient data is available in the first task to learn a high-quality backbone. This is espe…

Cited by 23Source ↗PDF ↗code ↗Scholar ↗