3 citations · 4 across the 21 of their papers we have counts for
9 papers · 1 filter
Rethinking Expert Training for Model Merging with Prompt Learning
Christos Georgakilas, Aniello Panariello, Samir El Karrat Moreno +3
Model merging aims to combine multiple domain-specialized experts trained from a shared foundation model into a single multi-task model. Existing approaches largely focus on improv…
Robust Zero-Shot Generalization for Open-Vocabulary Action Recognition via Task Arithmetic
Francesca Morandi, Omayma Moussadek, Federico Venturini +5
Open Vocabulary Action Recognition (OVAR) enables the recognition of novel actions by leveraging vision-language representations, overcoming the limitations of traditional closed-s…
Zero-Shot Synthetic-to-Real Handwritten Text Recognition via Task Analogies
Carlos Garrido-Munoz, Aniello Panariello, Silvia Cascianelli +4
Handwritten Text Recognition (HTR) models trained on synthetic handwriting often struggle to generalize to real text, and existing adaptation methods still require real samples fro…
ABRA: Teleporting Fine-Tuned Knowledge Across Domains for Open-Vocabulary Object Detection
Mattia Bernardi, Chiara Cappellino, Matteo Mosconi +3
Although recent Open-Vocabulary Object Detection architectures, such as Grounding DINO, demonstrate strong zero-shot capabilities, their performance degrades significantly under do…
Accurate and Efficient Low-Rank Model Merging in Core Space
Aniello Panariello, Daniel Marczak, Simone Magistri +5
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…
DitHub: A Modular Framework for Incremental Open-Vocabulary Object Detection
Chiara Cappellino, Gianluca Mancusi, Matteo Mosconi +3
Open-Vocabulary object detectors can generalize to an unrestricted set of categories through simple textual prompting. However, adapting these models to rare classes or reinforcing…
An Attention-based Representation Distillation Baseline for Multi-Label Continual Learning
Martin Menabue, Emanuele Frascaroli, Matteo Boschini +3
The field of Continual Learning (CL) has inspired numerous researchers over the years, leading to increasingly advanced countermeasures to the issue of catastrophic forgetting. Mos…
CLIP with Generative Latent Replay: a Strong Baseline for Incremental Learning
Emanuele Frascaroli, Aniello Panariello, Pietro Buzzega +3
With the emergence of Transformers and Vision-Language Models (VLMs) such as CLIP, fine-tuning large pre-trained models has recently become a prevalent strategy in Continual Learni…
Selective Attention-based Modulation for Continual Learning
Giovanni Bellitto, Federica Proietto Salanitri, Matteo Pennisi +5
We present SAM, a biologically-plausible selective attention-driven modulation approach to enhance classification models in a continual learning setting. Inspired by neurophysiolog…