85 citations · 154 across the 15 of their papers we have counts for
18 papers
On Large Multimodal Models as Open-World Image Classifiers
Alessandro Conti, Massimiliano Mancini, Enrico Fini +3
Traditional image classification requires a predefined list of semantic categories. In contrast, Large Multimodal Models (LMMs) can sidestep this requirement by classifying images…
Retrieval-enriched zero-shot image classification in low-resource domains
Nicola Dall'Asen, Yiming Wang, Enrico Fini +1
Low-resource domains, characterized by scarce data and annotations, present significant challenges for language and visual understanding tasks, with the latter much under-explored…
Automatic benchmarking of large multimodal models via iterative experiment programming
Alessandro Conti, Enrico Fini, Paolo Rota +3
Assessing the capabilities of large multimodal models (LMMs) often requires the creation of ad-hoc evaluations. Currently, building new benchmarks requires tremendous amounts of ma…
Vocabulary-free Image Classification and Semantic Segmentation
Alessandro Conti, Enrico Fini, Massimiliano Mancini +3
Large vision-language models revolutionized image classification and semantic segmentation paradigms. However, they typically assume a pre-defined set of categories, or vocabulary,…
Semi-supervised learning made simple with self-supervised clustering
Enrico Fini, Pietro Astolfi, Karteek Alahari +4
Self-supervised learning models have been shown to learn rich visual representations without requiring human annotations. However, in many real-world scenarios, labels are partiall…
Vocabulary-free Image Classification
Alessandro Conti, Enrico Fini, Massimiliano Mancini +3
Recent advances in large vision-language models have revolutionized the image classification paradigm. Despite showing impressive zero-shot capabilities, a pre-defined set of categ…
Improved baselines for vision-language pre-training
Enrico Fini, Pietro Astolfi, Adriana Romero-Soriano +2
Contrastive learning has emerged as an efficient framework to learn multimodal representations. CLIP, a seminal work in this area, achieved impressive results by training on paired…
StyLIP: Multi-Scale Style-Conditioned Prompt Learning for CLIP-based Domain Generalization
Shirsha Bose, Ankit Jha, Enrico Fini +3
Large-scale foundation models, such as CLIP, have demonstrated impressive zero-shot generalization performance on downstream tasks, leveraging well-designed language prompts. Howev…
A soft nearest-neighbor framework for continual semi-supervised learning
Zhiqi Kang, Enrico Fini, Moin Nabi +2
Despite significant advances, the performance of state-of-the-art continual learning approaches hinges on the unrealistic scenario of fully labeled data. In this paper, we tackle t…
Multimodal Emotion Recognition with Modality-Pairwise Unsupervised Contrastive Loss
Riccardo Franceschini, Enrico Fini, Cigdem Beyan +3
Emotion recognition is involved in several real-world applications. With an increase in available modalities, automatic understanding of emotions is being performed more accurately…
Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation
Guanglei Yang, Enrico Fini, Dan Xu +5
A fundamental and challenging problem in deep learning is catastrophic forgetting, i.e. the tendency of neural networks to fail to preserve the knowledge acquired from old tasks wh…
Continual Attentive Fusion for Incremental Learning in Semantic Segmentation
Guanglei Yang, Enrico Fini, Dan Xu +5
Over the past years, semantic segmentation, as many other tasks in computer vision, benefited from the progress in deep neural networks, resulting in significantly improved perform…
Self-Supervised Models are Continual Learners
Enrico Fini, Victor G. Turrisi da Costa, Xavier Alameda-Pineda +3
Self-supervised models have been shown to produce comparable or better visual representations than their supervised counterparts when trained offline on unlabeled data at scale. Ho…
A Unified Objective for Novel Class Discovery
Enrico Fini, Enver Sangineto, Stéphane Lathuilière +3
In this paper, we study the problem of Novel Class Discovery (NCD). NCD aims at inferring novel object categories in an unlabeled set by leveraging from prior knowledge of a labele…
Solo-learn: A Library of Self-supervised Methods for Visual Representation Learning
Victor G. Turrisi da Costa, Enrico Fini, Moin Nabi +2
This paper presents solo-learn, a library of self-supervised methods for visual representation learning. Implemented in Python, using Pytorch and Pytorch lightning, the library fit…
Neighborhood Contrastive Learning for Novel Class Discovery
Zhun Zhong, Enrico Fini, Subhankar Roy +3
In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes. We exploit the pe…
Online Continual Learning under Extreme Memory Constraints
Enrico Fini, Stéphane Lathuilière, Enver Sangineto +2
Continual Learning (CL) aims to develop agents emulating the human ability to sequentially learn new tasks while being able to retain knowledge obtained from past experiences. In t…
Supervised online diarization with sample mean loss for multi-domain data
Enrico Fini, Alessio Brutti
Recently, a fully supervised speaker diarization approach was proposed (UIS-RNN) which models speakers using multiple instances of a parameter-sharing recurrent neural network. In…