202 citations · 573 across the 58 of their papers we have counts for
43 papers · 1 filter
GeoBiaset: A Counterfactual Benchmark for Demographic Bias in World-Level Geolocalization
Niccolò Niccoli, Silvia Dani, Marco Mistretta +3
World-level image geolocalization is an increasingly important task, yet the biases of current models remain poorly understood. Prior work has identified coarse dataset imbalances…
WASP: Weakly Aligned Spatiotemporal Pairs for Fetal Brain MRI-Ultrasound Learning
Francesco Correnti, Gabriele Magrini, Marco Mistretta +6
Magnetic Resonance Imaging (MRI) is widely regarded as the optimal sensor for fetal brain analysis due to its superior soft-tissue contrast and anatomical detail. However, its high…
FLaRA: Predicting Future Latent Representations for Accident Anticipation
Lorenzo Caselli, Tomaso Trinci, Tommaso Bianconcini +4
Anticipating traffic accidents from dashcam videos is a critical challenge in intelligent transportation systems. Existing methods typically map visual context directly to a collis…
Project and Mix: Task-Semantic Prototypes for Few-Shot Image Classification
Dipam Goswami, Simone Magistri, Gido M. van de Ven +4
Vision-language models like CLIP are trained with the objective of aligning text and image pairs. Beyond text prompts alone, recent works show that exploiting few-shot image embedd…
IsoCLIP: Decomposing CLIP Projectors for Efficient Intra-modal Alignment
Simone Magistri, Dipam Goswami, Marco Mistretta +3
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 applie…
SpectralGCD: Spectral Concept Selection and Cross-modal Representation Learning for Generalized Category Discovery
Lorenzo Caselli, Marco Mistretta, Simone Magistri +1
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…
Training-Free Diffusion Priors for Text-to-Image Generation via Optimization-based Visual Inversion
Samuele Dell'Erba, Andrew D. Bagdanov
Diffusion models have established the state-of-the-art in text-to-image generation, but their performance often relies on a diffusion prior network to translate text embeddings int…
No MoCap Needed: Post-Training Motion Diffusion Models with Reinforcement Learning using Only Textual Prompts
Girolamo Macaluso, Lorenzo Mandelli, Mirko Bicchierai +2
Diffusion models have recently advanced human motion generation, producing realistic and diverse animations from textual prompts. However, adapting these models to unseen actions o…
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…
EFC++: Elastic Feature Consolidation with Prototype Re-balancing for Cold Start Exemplar-free Incremental Learning
Simone Magistri, Tomaso Trinci, Albin Soutif-Cormerais +2
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…
Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion
Marco Mistretta, Alberto Baldrati, Lorenzo Agnolucci +2
Pre-trained multi-modal Vision-Language Models like CLIP are widely used off-the-shelf for a variety of applications. In this paper, we show that the common practice of individuall…
ViewpointDepth: A New Dataset for Monocular Depth Estimation Under Viewpoint Shifts
Aurel Pjetri, Stefano Caprasecca, Leonardo Taccari +6
Monocular depth estimation is a critical task for autonomous driving and many other computer vision applications. While significant progress has been made in this field, the effect…
Improving Zero-shot Generalization of Learned Prompts via Unsupervised Knowledge Distillation
Marco Mistretta, Alberto Baldrati, Marco Bertini +1
Vision-Language Models (VLMs) demonstrate remarkable zero-shot generalization to unseen tasks, but fall short of the performance of supervised methods in generalizing to downstream…
Exemplar-free Continual Representation Learning via Learnable Drift Compensation
Alex Gomez-Villa, Dipam Goswami, Kai Wang +3
Exemplar-free class-incremental learning using a backbone trained from scratch and starting from a small first task presents a significant challenge for continual representation le…
EUFCC-340K: A Faceted Hierarchical Dataset for Metadata Annotation in GLAM Collections
Francesc Net, Marc Folia, Pep Casals +2
In this paper, we address the challenges of automatic metadata annotation in the domain of Galleries, Libraries, Archives, and Museums (GLAMs) by introducing a novel dataset, EUFCC…
Elastic Feature Consolidation for Cold Start Exemplar-Free Incremental Learning
Simone Magistri, Tomaso Trinci, Albin Soutif-Cormerais +2
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…
Class Incremental Learning with Pre-trained Vision-Language Models
Xialei Liu, Xusheng Cao, Haori Lu +3
With the advent of large-scale pre-trained models, interest in adapting and exploiting them for continual learning scenarios has grown. In this paper, we propose an approach to exp…
Masked Autoencoders are Efficient Class Incremental Learners
Jiang-Tian Zhai, Xialei Liu, Andrew D. Bagdanov +2
Class Incremental Learning (CIL) aims to sequentially learn new classes while avoiding catastrophic forgetting of previous knowledge. We propose to use Masked Autoencoders (MAEs) a…
Task-Adaptive Saliency Guidance for Exemplar-free Class Incremental Learning
Xialei Liu, Jiang-Tian Zhai, Andrew D. Bagdanov +2
Exemplar-free Class Incremental Learning (EFCIL) aims to sequentially learn tasks with access only to data from the current one. EFCIL is of interest because it mitigates concerns…
Exemplar-free Continual Learning of Vision Transformers via Gated Class-Attention and Cascaded Feature Drift Compensation
Marco Cotogni, Fei Yang, Claudio Cusano +2
We propose a new method for exemplar-free class incremental training of ViTs. The main challenge of exemplar-free continual learning is maintaining plasticity of the learner withou…
Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification
Kai Wang, Chenshen Wu, Andy Bagdanov +4
Lifelong object re-identification incrementally learns from a stream of re-identification tasks. The objective is to learn a representation that can be applied to all tasks and tha…
Long-Tailed Class Incremental Learning
Xialei Liu, Yu-Song Hu, Xu-Sheng Cao +3
In class incremental learning (CIL) a model must learn new classes in a sequential manner without forgetting old ones. However, conventional CIL methods consider a balanced distrib…
Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training
Simone Zini, Alex Gomez-Villa, Marco Buzzelli +3
Several recent works on self-supervised learning are trained by mapping different augmentations of the same image to the same feature representation. The data augmentations used ar…
Continually Learning Self-Supervised Representations with Projected Functional Regularization
Alex Gomez-Villa, Bartlomiej Twardowski, Lu Yu +2
Recent self-supervised learning methods are able to learn high-quality image representations and are closing the gap with supervised approaches. However, these methods are unable t…